Wafer cutting machine control method, electronic device, and storage medium

Abstract

The present application relates to the field of automation control technology, and in particular to a wafer cutting machine control method, electronic device, and storage medium. According to the wafer cutting machine control method of the present application, it is applied to control a wafer cutting machine, which includes a cutting tool. It is necessary to first obtain tool setting information matching the cutting tool, target sample parameters matching the target sample, and cutting control parameters; perform a pre-calibration operation on the wafer cutting machine to adjust the wafer cutting machine to a pre-operation state; determine cutting operation setting parameters for the wafer cutting machine based on the tool setting information, target sample parameters, and cutting control parameters; adjust the wafer cutting machine from the pre-operation state to the operation state, and perform a cutting operation on the target sample according to the cutting operation setting parameters to obtain a target wafer, thereby solving the problems of low efficiency caused by human factors and damage to equipment and samples caused by abnormal parameter settings, and bringing a higher degree of automation and reliability.

Description

Technical Field

[0001] This application relates to the field of automation control technology, and in particular to a wafer dicing machine control method, electronic equipment, and storage medium. Background Technology

[0002] In the traditional use of semi-automatic wafer dicing machines, the relevant technologies often rely on operators' memorization and manual execution of standard operating procedures (SOPs). Dicing parameters are set based on the operator's experience.

[0003] However, in actual operation, due to human factors such as forgetfulness or negligence, steps may be omitted, the order of operations may be incorrect, or parameter settings may be wrong, leading to sample scrapping or even equipment damage. Therefore, how to improve the accuracy and efficiency of operation of semi-automatic wafer dicing machines while maintaining high equipment flexibility and low R&D costs has become an urgent problem to be solved in the industry. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a wafer dicing machine control method, electronic equipment, and storage medium, which can solve the problems of low efficiency caused by human factors and damage to equipment and samples caused by abnormal parameter settings, bringing higher automation and reliability.

[0005] A wafer dicing machine control method according to a first aspect of this application is applied to control a wafer dicing machine, the wafer dicing machine including dicing blades, the method comprising:

[0006] Obtain tool setting information matching the cutting tool, target sample parameters matching the target sample, and cutting control parameters;

[0007] Perform a pre-calibration operation on the wafer dicing machine to adjust it to a pre-operation state;

[0008] Based on the tool setting information, the target sample parameters, and the cutting control parameters, the cutting operation setting parameters are determined for the wafer dicing machine through defined logical judgments;

[0009] The wafer dicing machine is adjusted from the pre-operation state to the operation state, and the dicing operation is performed on the target sample according to the dicing operation setting parameters to obtain the target wafer.

[0010] According to some embodiments of this application, obtaining tool setting information matching the cutting tool, target sample parameters matching the target sample, and cutting control parameters includes:

[0011] Acquire tool setting input information, target sample input parameters, and cutting control input parameters;

[0012] Based on the tool setting input information, the tool type corresponding to the cutting tool is determined;

[0013] Based on the tool type, data compliance checks are performed on the target sample input parameters and the cutting control input parameters to obtain data compliance check results;

[0014] If the data compliance test result meets the preset compliance test conditions, the tool setting information is determined based on the tool setting input information, the target sample parameters are determined based on the target sample input parameters, and the cutting control parameters are determined based on the cutting control input parameters.

[0015] According to some embodiments of this application, the step of performing data compliance detection on the target sample input parameters and the cutting control input parameters based on the tool type to obtain data compliance detection results includes:

[0016] Based on the target sample input parameters, the sample type is confirmed to obtain sample type input information;

[0017] Based on the cutting control input parameters, the feed rate is confirmed to obtain the feed rate input information;

[0018] Based on the tool type, the sample type input information is used for compliance testing to obtain the sample type test result;

[0019] Based on the tool type, the feed rate input information is checked for compliance, and the feed rate detection result is obtained;

[0020] Based on the sample type detection results and the feed rate detection results, the data compliance detection results are generated.

[0021] According to some embodiments of this application, the step of performing compliance testing on the sample type input information based on the tool type to obtain the sample type testing result includes:

[0022] In response to the fact that the tool type is a hard tool type, a compliance check is performed on the sample type input information. If the sample type input information includes sample types other than silicon wafers or silicon dioxide wafers, sample type abnormality information is generated as the sample type detection result.

[0023] The compliance check of the feed rate input information based on the tool type, to obtain the feed rate detection result, includes:

[0024] In response to the fact that the tool type is a hard tool, a compliance check is performed on the feed rate input information. If the feed rate input information is greater than a first preset speed value, feed rate abnormality information is generated as the feed rate detection result.

[0025] According to some embodiments of this application, the step of performing compliance testing on the sample type input information based on the tool type to obtain the sample type testing result includes:

[0026] In response to the fact that the tool type is a soft tool type, a compliance check is performed on the sample type input information. If the sample type input information includes sample types other than quartz plates, glass plates, or sapphire plates, sample type abnormality information is generated as the sample type detection result.

[0027] The compliance check of the feed rate input information based on the tool type, to obtain the feed rate detection result, includes:

[0028] In response to the fact that the tool type is a soft tool, a compliance check is performed on the feed rate input information. If the feed rate input information is greater than a second preset speed value, feed rate abnormality information is generated as the feed rate detection result.

[0029] According to some embodiments of this application, after performing data compliance detection on the target sample input parameters and the cutting control input parameters based on the tool type and obtaining the data compliance detection result, the method further includes:

[0030] If the data compliance test result does not meet the compliance test conditions, a data anomaly feedback operation is performed;

[0031] After performing the data anomaly feedback operation, the process returns to reacquire the target sample input parameters and the cutting control input parameters until the data compliance detection result meets the compliance detection conditions. Based on the tool setting input information, the tool setting information is determined; based on the reacquired target sample input parameters, the target sample parameters are determined; and based on the reacquired cutting control input parameters, the cutting control parameters are determined.

[0032] According to some embodiments of this application, the wafer dicing machine further includes a worktable and a height sensor. The step of performing a pre-calibration operation on the wafer dicing machine to adjust it to a pre-operation state includes:

[0033] The target sample is placed in the working area of ​​the worktable, and the working area is subjected to workpiece vacuum treatment to fix the target sample.

[0034] The height of the workbench surface is measured using the height sensor to obtain height calibration data.

[0035] Based on the height measurement calibration data, the wafer dicing machine is controlled to perform height measurement calibration to adjust the wafer dicing machine to a pre-operation state.

[0036] According to some embodiments of this application, the step of measuring the height of the workbench surface based on the height sensor to obtain height calibration data includes:

[0037] Retrieve the height measurement calibration prompt instruction from the preset operation procedure database;

[0038] The height measurement prompt instruction is executed to perform a height measurement calibration prompt operation, instructing the target user to control the height measurement sensor to perform a height measurement operation on the surface of the workbench and obtain the height measurement calibration data.

[0039] According to some embodiments of this application, the step of controlling the wafer dicing machine to perform height calibration based on the height calibration data to adjust the wafer dicing machine to a pre-operation state includes:

[0040] Retrieve the height measurement calibration prompt instruction from the preset operation procedure database;

[0041] The height measurement prompt instruction is executed to perform a height measurement calibration prompt operation, instructing the target user to control the wafer dicing machine to perform height measurement calibration based on the height measurement calibration data, so as to adjust the wafer dicing machine to the pre-operation state.

[0042] According to some embodiments of this application, determining the cutting operation settings parameters for the wafer dicing machine based on the tool setting information, the target sample parameters, and the cutting control parameters includes:

[0043] Based on the cutting tool parameters, the tool type and size information of the cutting tool are determined;

[0044] Based on the target sample parameters, determine the sample type and size information of the target sample;

[0045] Based on the cutting control parameters, the cutting type information, blade height parameters, feed speed information, and cutting step information are determined.

[0046] Based on the sample size information, the cutting type information, the blade height parameter, the feed speed information, and the cutting step information, the cutting operation settings parameters are determined for the wafer dicing machine.

[0047] According to some embodiments of this application, the step of adjusting the wafer dicing machine from the pre-operation state to the operation state, and performing a dicing operation on the target sample according to the dicing operation setting parameters to obtain the target wafer, further includes:

[0048] During the cutting operation on the target sample, the cutting machine equipment data is collected in real time, and the tool setting information, target sample parameters and cutting control parameters that match the cutting machine equipment data are obtained.

[0049] Based on the cutting machine equipment data, the tool setting information matched with the cutting machine equipment data, the target sample parameters, and the cutting control parameters, the operation process is analyzed to obtain operation process analysis data.

[0050] According to some embodiments of this application, performing a cutting operation on the target sample according to the cutting operation setting parameters includes:

[0051] During the execution of the cutting operation, a job monitoring operation is performed;

[0052] In response to the capture of a violation operation by the operation monitoring, a matching violation handling operation is determined based on the violation operation type of the violation operation;

[0053] After determining the violation handling operation that matches the violation operation type, the violation handling operation is executed to handle the violation operation.

[0054] According to some embodiments of this application, the job monitoring operation involves capturing an operation monitoring screen on the corresponding operation terminal of the cutting operation, and the method further includes:

[0055] In response to an alarm notification captured on the operation monitoring screen, determine the current time point;

[0056] Determine the preceding and following time intervals of the current time node;

[0057] Based on the preceding time interval and the following time interval, the target monitoring screen is extracted from the operation monitoring screen, and the target monitoring screen is identified as alarm association record information;

[0058] Compliance analysis is performed based on the alarm association record information to obtain alarm association analysis results.

[0059] According to some embodiments of this application, the method further includes:

[0060] During the execution of the cutting operation, the current operation process is determined in real time;

[0061] Based on the current operation process, a comparison is performed with a preset operation specification benchmark to determine the currently disabled control that matches the current operation process; wherein, the operation specification benchmark records multiple preset operation processes and the type of disabled control that matches each preset operation process;

[0062] On the operation terminal corresponding to the cutting operation, a disable / lock operation is performed on the currently disabled control.

[0063] According to some embodiments of this application, after performing a cutting operation on the target sample according to the cutting operation setting parameters to obtain the target wafer, the method further includes:

[0064] Obtain the parameter parsing requirements and the actual cutting parameters collected for the cutting operation;

[0065] Based on the parameter analysis requirements, the actual collected parameters for the cutting are analyzed to obtain the analysis result data.

[0066] According to some embodiments of this application, the step of parsing the actual collected parameters for cutting based on the parameter parsing requirements to obtain the requirement parsing result data includes:

[0067] In response to the parameter parsing requirement being a consumable cost parsing requirement, the unit price of consumables corresponding to various preset consumable types is obtained;

[0068] Based on the actual cutting parameters collected, consumable parameters are extracted to obtain the actual consumable parameters;

[0069] Cost calculation is performed based on the actual consumable parameters and the unit price of the consumables to obtain the demand analysis result data that matches the demand analysis requirements for the consumables cost.

[0070] Secondly, embodiments of this application provide an electronic device, including: a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the wafer dicing machine control method as described in any one of the embodiments of the first aspect of this application.

[0071] Thirdly, embodiments of this application provide a computer-readable storage medium storing a program that is executed by a processor to implement a wafer dicing machine control method as described in any one of the embodiments of the first aspect of this application.

[0072] The wafer dicing machine control method, electronic device, and storage medium according to the embodiments of this application have at least the following beneficial effects:

[0073] The wafer dicing machine control method of this application is applied to control a wafer dicing machine, which includes dicing blades. The method requires first acquiring blade setting information matched to the dicing blades, target sample parameters matched to the target sample, and dicing control parameters. A pre-calibration operation is performed on the wafer dicing machine to adjust it to a pre-operation state. Based on the blade setting information, target sample parameters, and dicing control parameters, dicing operation setting parameters are determined for the wafer dicing machine. The wafer dicing machine is then adjusted from the pre-operation state to the operation state, and dicing is performed on the target sample according to the dicing operation setting parameters to obtain the target wafer. This wafer dicing machine control method effectively solves the problems of low efficiency caused by human factors and equipment or sample damage caused by abnormal parameter settings in traditional operations by systematically acquiring and applying key parameters, performing pre-calibration operations, and automatically completing the software operation process. While ensuring compatibility with diverse samples, it brings a higher degree of automation and reliability to the operation of semi-automatic wafer dicing machines.

[0074] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0075] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0076] Figure 1 A schematic flowchart of a wafer dicing machine control method provided in an embodiment of this application;

[0077] Figure 2 This is another schematic flowchart of the wafer dicing machine control method provided in the embodiments of this application;

[0078] Figure 3 This is another schematic flowchart of the wafer dicing machine control method provided in the embodiments of this application;

[0079] Figure 4 This is another schematic flowchart of the wafer dicing machine control method provided in the embodiments of this application;

[0080] Figure 5 This is another schematic flowchart of the wafer dicing machine control method provided in the embodiments of this application;

[0081] Figure 6 This is another schematic flowchart of the wafer dicing machine control method provided in the embodiments of this application;

[0082] Figure 7 This is another schematic flowchart of the wafer dicing machine control method provided in the embodiments of this application;

[0083] Figure 8 This is another schematic flowchart of the wafer dicing machine control method provided in the embodiments of this application;

[0084] Figure 9 This is another schematic flowchart of the wafer dicing machine control method provided in the embodiments of this application;

[0085] Figure 10 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0086] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0087] In the description of this application, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0088] In the description of this application, it should be understood that the orientation descriptions, such as up, down, left, right, front, and back, are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0089] In the description of this specification, the terms "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. Furthermore, the identification of specific steps below does not imply a limitation on the order of steps or execution logic; the execution order and logic between the steps should be understood and inferred from the content described in the embodiments.

[0090] In the traditional use of wafer dicing machines, the relevant technologies often rely on operators' memorization and manual execution of standard operating procedures (SOPs).

[0091] However, in practice, due to human factors such as forgetfulness or negligence, steps are easily omitted or the order of operations is incorrect. In operations requiring manual intervention, such as manually aligning samples or adjusting image focus, operators may waste considerable time making repeated adjustments due to improper operation, reducing equipment efficiency. Therefore, how to solve the problems of inefficiency caused by human factors and damage to equipment and samples due to abnormal parameter settings, and achieve higher automation and reliability, has become an urgent issue to be addressed in the industry.

[0092] It is worth noting that, compared to fully automatic wafer dicing machines, semi-automatic wafer dicing machines are more suitable for R&D, pilot production, or low-volume needs. However, the semi-automatic wafer dicing machines in current related technologies are highly dependent on operator skills. The embodiments of this application can also effectively solve this problem.

[0093] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a wafer dicing machine control method, electronic equipment, and storage medium, which can solve the problems of low efficiency caused by human factors and damage to equipment and samples caused by abnormal parameter settings, bringing higher automation and reliability.

[0094] The following explanation is based on the accompanying drawings.

[0095] Reference Figure 1 The wafer dicing machine control method according to the embodiments of this application is applied to control a wafer dicing machine, which may include dicing blades. The wafer dicing machine control method according to the embodiments of this application may include:

[0096] Step S101: Obtain tool setting information matching the cutting tool, target sample parameters matching the target sample, and cutting control parameters;

[0097] Step S102: Perform a pre-calibration operation on the wafer dicing machine to adjust the wafer dicing machine to a pre-operation state;

[0098] Step S103: Based on the tool setting information, target sample parameters, and cutting control parameters, determine the cutting operation setting parameters for the wafer dicing machine;

[0099] Step S104: Adjust the wafer dicing machine from the pre-operation state to the operation state, and perform the dicing operation on the target sample according to the dicing operation settings to obtain the target wafer.

[0100] In some embodiments, step S101 involves obtaining tool setting information matching the cutting tool, target sample parameters matching the target sample, and cutting control parameters.

[0101] It is important to note that obtaining tool setting information that matches the cutting tool, sample parameters that match the target sample, and cutting control parameters are fundamental to achieving precise control of a wafer dicing machine. Tool setting information includes key data such as the type, size, material, and wear level of the cutting tool. This information is crucial for determining the tool's applicability and cutting capabilities. For example, soft resin tools are suitable for easily cracked materials such as glass, while hard metal tools are better suited for high-precision and high-hardness cutting tasks. By obtaining this information, it is possible to ensure that the selected tool matches the material and cutting requirements of the target sample, thereby avoiding cutting failures or equipment damage due to unsuitable tools.

[0102] It should be noted that the target sample parameters encompass the physical properties and process requirements of the target sample, such as its thickness, size, shape, material, and surface treatment requirements. These parameters directly affect tool path planning, depth-of-cut control, and cutting speed selection during the cutting process. For example, thicker samples may require adjustments to the tool feed rate and depth of cut to ensure cut integrity and accuracy. Different materials cause varying degrees of tool wear; therefore, appropriate cutting parameters must be selected based on the material to extend tool life and ensure cutting quality.

[0103] It should be noted that cutting control parameters include operating parameters such as spindle speed, feed rate, step distance, and depth of cut. Proper setting of these parameters is crucial for achieving an efficient and precise cutting process. Matching the spindle speed and feed rate ensures a smooth and efficient cutting process, while appropriate step distance and depth of cut help avoid overcutting or undercutting.

[0104] Reference Figure 2 According to some embodiments of this application, step S101, which involves obtaining tool setting information matching the cutting tool, target sample parameters matching the target sample, and cutting control parameters, may include:

[0105] Step S201: Obtain tool setting input information, target sample input parameters, and cutting control input parameters;

[0106] Step S202: Based on the tool setting input information, determine the tool type corresponding to the cutting tool;

[0107] Step S203: Based on the tool type, perform data compliance checks on the target sample input parameters and cutting control input parameters to obtain the data compliance check results;

[0108] Step S204: If the data compliance test results meet the preset compliance test conditions, determine the tool setting information based on the tool setting input information, determine the target sample parameters based on the target sample input parameters, and determine the cutting control parameters based on the cutting control input parameters.

[0109] In some embodiments, step S201 involves acquiring tool setting input information, target sample input parameters, and cutting control input parameters;

[0110] It should be noted that the embodiments of this application first acquire tool setting input information, target sample input parameters, and cutting control input parameters. This input information can come from operator input via a user interface or preset values ​​retrieved from the wafer dicing machine's database. The tool setting input information may include key parameters such as the type, size, material, brand, and model of the cutting tool; these parameters directly affect the feasibility and efficiency of the cutting process. The target sample input parameters may cover the physical characteristics of the sample, such as thickness, size, shape, and material; this information is crucial for determining the cutting path and cutting conditions. The cutting control input parameters may involve operational parameters such as spindle speed, feed rate, step distance, and cutting depth; these parameters directly affect the accuracy and efficiency of the cutting process.

[0111] In some embodiments, step S202 involves determining the tool type corresponding to the cutting tool based on the tool setting input information.

[0112] It should be noted that the tool type corresponding to the cutting tool is determined based on the acquired tool setting input information. This step identifies the specific tool type by analyzing the input tool setting information, such as pure resin soft tool, high-rigidity resin soft tool, metal hard tool, and ceramic hard tool. Correct tool type identification is crucial for subsequent data compliance testing and cutting parameter determination, as it ensures that the selected tool matches the material and cutting requirements of the target sample.

[0113] In some embodiments, step S203 involves performing data compliance checks on the target sample input parameters and cutting control input parameters based on the tool type, and obtaining data compliance check results.

[0114] It should be noted that, based on the determined tool type, data compliance checks are performed on the target sample input parameters and cutting control input parameters. The purpose of this step is to verify whether the input parameters conform to preset rules and conditions, such as whether the tool type is suitable for the target sample material and whether the cutting speed is within the tool's allowable range. Data compliance checks, through a series of logical judgments and verification rules, ensure that all input parameters are within reasonable and safe ranges to avoid cutting failures or equipment damage due to improper parameter settings.

[0115] Reference Figure 3 According to some embodiments of this application, step S203, based on the tool type, performs data compliance checks on the target sample input parameters and cutting control input parameters to obtain data compliance check results, and may include:

[0116] Step S301: Based on the target sample input parameters, confirm the sample type to obtain sample type input information;

[0117] Step S302: Confirm the feed rate based on the cutting control input parameters to obtain the feed rate input information;

[0118] Step S303: Perform compliance testing on the sample type input information based on the tool type to obtain the sample type test result;

[0119] Step S304: Perform compliance checks on the feed rate input information based on the tool type to obtain the feed rate detection result;

[0120] Step S305: Based on the sample type detection results and the feed rate detection results, generate data compliance detection results.

[0121] In some embodiments, step S301 involves confirming the sample type based on the target sample input parameters to obtain sample type input information.

[0122] It's important to note that sample type confirmation is performed based on the target sample input parameters to obtain sample type input information. This step determines the specific type of the target sample by analyzing the input sample parameters, such as thickness, size, shape, and material. For example, the sample might be a silicon wafer, glass sheet, or other types of material. Correct sample type confirmation is crucial for subsequent compliance testing because it ensures that the selected cutting tools and cutting parameters match the physical properties of the sample.

[0123] In some embodiments, step S302 involves confirming the feed rate based on the cutting control input parameters to obtain feed rate input information.

[0124] It should be noted that the feed rate is confirmed based on the cutting control input parameters to obtain the feed rate input information. Feed rate is a critical operating parameter in the cutting process, directly affecting cutting efficiency and quality. Some embodiments of the wafer dicing machine in this application require confirmation that the input feed rate is within a reasonable range and suitable for the selected tool and sample type.

[0125] In some embodiments, step S303 involves performing a compliance check on the sample type input information based on the tool type to obtain the sample type detection result.

[0126] It's important to note that compliance checks are performed on the sample type input information based on the tool type to obtain the sample type detection results. This step uses logical judgments and verification rules to check whether the selected tool is suitable for the given sample type. For example, if the tool type is a diamond tool and the sample type is a glass plate, a mismatch may be detected and marked as non-compliant. Compliance checks ensure compatibility between the tool and the sample, preventing cutting failures or equipment damage due to incompatibility.

[0127] In some embodiments, step S304 involves performing a compliance check on the feed rate input information based on the tool type to obtain the feed rate detection result.

[0128] It should be noted that the feed rate input information is checked for compliance based on the tool type, resulting in a feed rate detection result. This step checks whether the input feed rate is within the allowable range of the selected tool and whether it is suitable for the physical characteristics of the target sample. For example, if the feed rate is too high, it may lead to accelerated tool wear or sample damage, therefore non-compliant speed settings will be detected and flagged.

[0129] Reference Figure 4 According to some embodiments of this application, step S303, which performs compliance testing on the sample type input information based on the tool type to obtain the sample type testing result, may include:

[0130] Step S401: In response to the tool type being a hard tool type, a compliance check is performed on the sample type input information. If the sample type input information can include sample types other than silicon wafers or silicon dioxide wafers, sample type abnormality information is generated as the sample type check result.

[0131] In step S304, the feed rate input information is checked for compliance based on the tool type to obtain the feed rate detection result, which may include:

[0132] In step S402, in response to the tool type being a hard tool, a compliance check is performed on the feed rate input information. If the feed rate input information is greater than the first preset speed value, feed rate abnormality information is generated as the feed rate detection result.

[0133] In step S401 of some embodiments, in response to the tool type being a hard tool type, a compliance check is performed on the sample type input information. If the sample type input information may include sample types other than silicon wafers or silicon dioxide wafers, sample type abnormality information is generated as the sample type detection result.

[0134] It should be noted that when the blade type is a hard blade, this embodiment of the application performs a compliance check on the sample type input information. Ordinary hard blades are typically designed for cutting harder materials, such as silicon wafers and silicon dioxide wafers. Therefore, this embodiment of the application checks whether the sample type input information includes materials suitable for hard blade cutting. If the sample type input information contains types other than silicon wafers or silicon dioxide wafers, such as glass sheets or sapphire sheets, this embodiment of the application will identify this mismatch and generate sample type anomaly information as the sample type detection result. This mechanism ensures that the cutting operation is only allowed when the sample type matches the applicable scope of the hard blade, thereby avoiding cutting failures or equipment damage caused by using an improper blade.

[0135] In step S402 of some embodiments, in response to the tool type being a hard tool type, a compliance check is performed on the feed rate input information. If the feed rate input information is greater than a first preset speed value, feed rate abnormality information is generated as the feed rate detection result.

[0136] It should be noted that for hard-blade cutting tools, the feed rate is a critical parameter, directly affecting tool wear and cutting quality during the cutting process. Excessive feed rate can lead to excessive tool wear or even damage, while also reducing cutting quality. Therefore, this embodiment sets a first preset speed value as the maximum permissible feed rate for hard-blade cutting tools. When the feed rate input exceeds this preset value, an anomaly is detected, and feed rate anomaly information is generated as the feed rate detection result. This detection step ensures that the feed rate is always kept within the safe range allowed by the tool design, thereby protecting the tool and equipment, while also ensuring the stability of the cutting process and the accuracy of the results.

[0137] This application's embodiments, through these meticulous compliance testing steps, effectively prevent potential problems caused by operator errors or a lack of understanding of the applicable tool range. These steps demonstrate the intelligent characteristics of the wafer dicing machine control method, reducing the possibility of human error and ensuring the accuracy and rationality of dicing parameters through automated detection and early warning mechanisms.

[0138] Reference Figure 5 According to some embodiments of this application, step S303, which performs compliance testing on the sample type input information based on the tool type to obtain the sample type testing result, may include:

[0139] Step S501: In response to the tool type being a soft tool type, a compliance check is performed on the sample type input information. If the sample type input information can include sample types other than quartz plates, glass plates, or sapphire plates, sample type abnormality information is generated as the sample type check result.

[0140] In step S304, the feed rate input information is checked for compliance based on the tool type to obtain the feed rate detection result, which may include:

[0141] In step S502, in response to the tool type being a soft tool, a compliance check is performed on the feed rate input information. If the feed rate input information is greater than the second preset speed value, feed rate abnormality information is generated as the feed rate detection result.

[0142] In some embodiments, step S501, in response to the tool type being a soft tool type, performs a compliance check on the sample type input information. If the sample type input information may include sample types other than quartz plates, glass plates, or sapphire plates, then sample type abnormality information is generated as the sample type detection result.

[0143] It should be noted that when the blade type is a soft blade, this embodiment of the application will perform a compliance check on the sample type input information. Resin soft blades are typically designed for cutting brittle materials, such as quartz sheets and glass sheets. Therefore, this embodiment of the application will check whether the sample type input information includes these materials suitable for soft blade cutting. If the sample type input information contains types other than quartz sheets, glass sheets, or sapphire sheets, such as silicon sheets or silicon dioxide sheets, this embodiment of the application will identify this mismatch and generate sample type anomaly information as the sample type detection result. This mechanism ensures that the cutting operation is only allowed when the sample type matches the applicable scope of the specific soft blade purchased, thereby avoiding cutting failures or equipment damage caused by the use of improper blades.

[0144] In some embodiments, step S502, in response to the tool type being a soft tool type, performs a compliance check on the feed rate input information. If the feed rate input information is greater than a second preset speed value, feed rate abnormality information is generated as the feed rate detection result.

[0145] It should be noted that for soft-blade cutting, the feed rate is a critical parameter, directly affecting tool wear and cutting quality during the cutting process. Excessive feed rate can lead to excessive tool wear or even damage, while also reducing cutting quality. Therefore, this embodiment sets a second preset speed value as the maximum permissible feed rate for soft-blade cutting. When the feed rate input exceeds this preset value, this embodiment detects an anomaly and generates feed rate anomaly information as the feed rate detection result. This detection step ensures that the feed rate is always kept within the safe range allowed by the tool design, thereby protecting the tool and equipment, while also ensuring the stability of the cutting process and the accuracy of the results.

[0146] This application's embodiments, through these meticulous compliance testing steps, effectively prevent potential problems caused by operator errors or a lack of understanding of the applicable tool range. These steps demonstrate the intelligent characteristics of the wafer dicing machine control method, reducing the possibility of human error and ensuring the accuracy and rationality of dicing parameters through automated detection and early warning mechanisms.

[0147] In some more specific embodiments, the selection of blade type and the matching of sample type and feed speed are crucial during the operation of the wafer dicing machine.

[0148] When the hard blade type is selected, the blade is specifically designed for cutting samples of harder materials, such as silicon wafers and silicon dioxide wafers. The sample type allows the user to select either a silicon wafer or a silicon dioxide wafer as the target sample. This is because a hard blade maintains a sharp cutting edge better when cutting materials with higher hardness, ensuring cutting quality and efficiency. If other sample types are selected, this embodiment will identify the mismatch and generate sample type anomaly information as a detection result, prompting for correction. Simultaneously, to avoid excessive blade wear or sample damage due to excessive speed when cutting hard materials, the hard blade has an upper limit of 20 mm / s on the feed rate. If the input feed rate exceeds this value, a warning will be issued, prompting adjustment to a safe range.

[0149] The soft-blade type is designed for cutting brittle materials such as quartz and glass. These materials are relatively soft, requiring a soft blade to prevent surface damage or excessive blade wear due to hardness mismatch. This application also detects the sample type to ensure only quartz and glass are selected for cutting. An error message is issued immediately if other sample types are input. Regarding feed rate, the speed must be strictly controlled below 3 mm / s when cutting soft materials. This is because soft materials are prone to cracking or chipping due to excessive speed, affecting cutting quality. This application monitors the feed rate in real time; if overspeed is detected, an immediate warning is issued, requiring adjustment to a suitable value.

[0150] It should be understood that the specific types of cutting tools mentioned in this application are not limited to the examples above. These examples are mainly for the purpose of facilitating understanding and illustrating the specific application scenarios and operational logic of the embodiments of this application under different types of cutting tools. However, in practical applications, there are far more than just these two types of cutting tools. With the continuous development of materials science, cutting tools made of new materials are constantly emerging. These cutting tools each have unique physical and chemical properties, thus being able to meet the needs of cutting samples of different types and characteristics.

[0151] Different types of cutting tools made of different materials are indeed suitable for cutting different types of samples. For example, carbide tools, due to their high hardness and wear resistance, are generally suitable for cutting hard metals or ceramics; while diamond tools, due to their extremely high hardness, are often used to cut superhard materials such as gemstones and carbide. In contrast, tools made of some softer materials, such as certain polymer tools, may be more suitable for cutting more fragile or brittle samples to avoid unnecessary damage during the cutting process. This difference is mainly determined by the physical properties of the tool material itself, such as hardness, toughness, and wear resistance. Different sample materials require matching tools to achieve efficient cutting while ensuring cutting quality and sample integrity. There are numerous types of cutting tools and their corresponding sample types, which will not be listed here.

[0152] In some embodiments, step S305 generates a data compliance detection result based on the sample type detection result and the feed rate detection result.

[0153] It should be noted that an overall data compliance check result is generated based on the sample type test results and the feed rate test results. This step integrates the test results from the previous two steps to determine whether all input parameters meet the preset compliance conditions. If all parameters pass the compliance check, a compliance check result will be generated, allowing subsequent cutting operations to continue. Conversely, if non-compliant parameters are found, the operator will be prompted to make adjustments to ensure the safety and effectiveness of the cutting process.

[0154] This application's embodiments, through this series of compliance testing steps, effectively verify the correctness and safety of input parameters, ensuring that the wafer dicing machine uses appropriate tools and parameter settings when performing dicing operations. This not only improves dicing accuracy and efficiency but also reduces equipment wear and sample damage caused by improper parameter settings, providing reliable technical support for achieving automated and intelligent wafer dicing.

[0155] In some embodiments, step S204 involves determining tool setting information based on tool setting input information, determining target sample parameters based on target sample input parameters, and determining cutting control parameters based on cutting control input parameters, provided that the data compliance detection result meets preset compliance detection conditions.

[0156] It should be noted that, provided the data compliance test results meet the preset compliance test conditions, the final tool setting information is determined based on the tool setting input information, the target sample parameters are determined based on the target sample input parameters, and the cutting control parameters are determined based on the cutting control input parameters. This step marks the completion of the parameter acquisition and verification process, ensuring that subsequent cutting operations will be performed based on accurate and compliant parameters. Through this series of steps, the embodiments of this application can provide precise and reliable parameter configuration for wafer dicing machines, thereby achieving efficient and accurate cutting operations.

[0157] It should be understood that the embodiments of this application ensure the accuracy and reliability of the cutting operation through systematic parameter acquisition and verification. This not only reduces human error but also improves the operating efficiency of the equipment and the cutting quality, providing advanced technical support for precision machining fields such as semiconductor manufacturing.

[0158] Reference Figure 6 According to some embodiments of this application, after step S203 performs data compliance detection on the target sample input parameters and cutting control input parameters based on the tool type and obtains the data compliance detection result, it may further include:

[0159] Step S601: If the data compliance test result does not meet the compliance test conditions, execute the data anomaly feedback operation;

[0160] Step S602: After performing the data anomaly feedback operation, return to reacquire the target sample input parameters and cutting control input parameters until the data compliance test result meets the compliance test conditions. Determine the tool setting information based on the tool setting input information, determine the target sample parameters based on the reacquired target sample input parameters, and determine the cutting control parameters based on the reacquired cutting control input parameters.

[0161] In some embodiments, step S601 involves performing a data anomaly feedback operation if the data compliance detection result does not meet the compliance detection conditions.

[0162] It should be noted that after the data compliance check, this embodiment will determine whether the check result meets the preset compliance conditions. If not, indicating improper or mismatched parameter settings, this embodiment will perform a data anomaly feedback operation. The purpose of this step is to promptly notify the operator that the current parameters are abnormal and need to be adjusted. Anomaly feedback can be provided in various ways, such as displaying a warning dialog box on the user interface, highlighting the abnormal parameters, and providing detailed error information and suggested corrective measures. Such a feedback mechanism ensures that the operator can quickly identify the problem and take corresponding corrective measures.

[0163] In some embodiments, step S602 involves returning to reacquire the target sample input parameters and cutting control input parameters after performing the data anomaly feedback operation, until the data compliance test result meets the compliance test conditions. The tool setting information is determined based on the tool setting input information, the target sample parameters are determined based on the reacquired target sample input parameters, and the cutting control parameters are determined based on the reacquired cutting control input parameters.

[0164] It should be noted that after performing the data anomaly feedback operation, this embodiment does not immediately terminate the operation process, but rather gives the operator an opportunity to correct the parameters. This embodiment will return to the parameter acquisition stage to reacquire the target sample input parameters and cutting control input parameters. This step allows the operator to adjust and correct the previous input parameters based on the anomaly feedback information. This embodiment will then perform a compliance check on the corrected parameters again to verify whether they meet the conditions. This cyclical process will continue until all parameters pass the compliance check, ensuring that the cutting operation can be performed under safe and precise conditions.

[0165] It should be understood that the above design reflects the intelligence and user-friendliness of the embodiments of this application. Through real-time feedback and correction mechanisms, the risk of operation failure or equipment damage due to parameter errors is reduced. Simultaneously, it improves operational efficiency, as operators can quickly correct errors under the guidance of the embodiments of this application, avoiding tedious manual troubleshooting processes. Furthermore, this process enhances the robustness of the embodiments of this application, enabling them to adapt to different operating scenarios and parameter inputs, ensuring the stable operation of the wafer dicing machine under various conditions.

[0166] In summary, through the mechanisms of data anomaly feedback and parameter reacquisition, the embodiments of this application provide a reliable quality assurance measure for the operation of wafer dicing machines. This not only improves equipment safety and operational efficiency but also lowers the technical threshold for operators, making complex wafer dicing processes easier to manage and execute. These characteristics have significant application value for modern semiconductor manufacturing and other precision processing fields.

[0167] In some more specific embodiments, step S203 performs data compliance checks on the target sample input parameters and cutting control input parameters based on the tool type, and obtains the data compliance check results. It may also include ensuring the correct setting of parameters such as film type, blade height, sample shape and size, number of cutting strips and spindle speed during the operation of the wafer dicing machine, which is crucial for achieving efficient and accurate cutting.

[0168] First, the choice of film type is closely related to the sample thickness. When the sample thickness is less than 1 mm, the operator can choose either blue film or UV film as the film type. Both types of films can provide the necessary protection for the sample, preventing damage during the cutting process. However, when the sample thickness exceeds 1 mm, UV film becomes the only option. This is because UV film exhibits better stability and protective performance during the cutting of thicker samples, effectively reducing sample deformation and damage.

[0169] Secondly, the blade height setting needs to be adjusted according to the type of film applied. If a blue film is selected, the blade height must be no less than 0.06 mm; if a UV film is selected, the blade height must be at least 0.11 mm. These values ​​are set to ensure that the blade can effectively cut through the sample, while avoiding incomplete cutting or sample damage due to insufficient blade height. This embodiment monitors the input value of the blade height in real time and issues a warning when it detects a non-compliance, prompting the operator to make adjustments.

[0170] Regarding the relationship between the blade protrusion and the sample thickness, this application provides a set of logical judgment rules. If the value of the blade protrusion minus the protective allowance (usually 0.05 mm) is greater than or equal to the sample thickness, then the blade height only needs to meet the minimum blade height requirements related to the film application. In this case, the blade height is sufficient to cover the sample thickness, ensuring smooth cutting. However, if the value of the blade protrusion minus the protective allowance is less than the sample thickness, the blade height needs to be further increased to ensure that the blade can completely penetrate the sample. In this case, the formula for calculating the blade height is: Blade height ≥ Sample thickness + Film thickness - (Blade protrusion - Protective allowance 0.01). This rule ensures that the blade can reach sufficient depth during cutting, while avoiding damage to the sample and blade due to over-cutting.

[0171] Regarding the input of sample shape and size, this application provides specific guidance based on different shape types. Considering that the blade needs to start cutting from an area 3-5mm away from the sample, the dimensions entered into the device need to be larger than the actual sample size. When the sample shape is circular, the input value is a common sample size (2, 4, 6, 8 inches), and the corresponding cutting distance will be automatically calculated as 60, 110, 160, and 205mm respectively. These values ​​are empirical values, ensuring accuracy and consistency during the cutting process. For non-standard sized circular samples, it is difficult to accurately center them on the workpiece holder. When the size is less than 150mm, this application requires an increase of 20mm from the input value; when the size is greater than 150mm, this application requires an increase of 10mm from the input value to ensure a safe margin for the cutting path. For square samples, the operator needs to enter the specific values ​​for length and width separately, and both values ​​must be less than 196mm. This application will increase the input length and width by 10mm each to ensure the stability and safety of the cutting operation.

[0172] The number of cuts needs to be calculated based on the sample size and step distance. This embodiment automatically calculates the maximum number of cuts based on the sample size and step distance, and requires the operator to input no more than this maximum number. For example, if the sample size is 150mm and the step distance is 20mm, this embodiment calculates a maximum of 7 cuts. This limitation ensures the feasibility of the cutting task and avoids equipment overload or cutting quality problems caused by an excessive number of cuts.

[0173] Finally, the spindle speed setting needs to be adjusted according to the type of cutting tool. Hard cutting tools are suitable for higher speeds, with a maximum permissible speed of 35,000 r / min; while soft cutting tools, due to their design characteristics and the different materials they are made of, have a maximum permissible speed of 25,000 r / min. This distinction ensures the stability and durability of the cutting tool during the cutting process, while also protecting the equipment from potential damage caused by excessively high speeds.

[0174] In some embodiments, step S102 involves performing a job pre-calibration operation on the wafer dicing machine to adjust the wafer dicing machine to a pre-job state.

[0175] It should be noted that the core of the pre-calibration operation is to adjust the wafer dicing machine from its initial state to a known and stable pre-operation state, providing a standardized starting point for subsequent dicing operations.

[0176] In some embodiments, pre-calibration operations may include initializing the wafer dicing machine. This step involves resetting various control parameters of the equipment to factory settings or preset initial states, eliminating variables and interference factors that may have been left over from previous operations. Initialization ensures the coordination and consistency between the various components of the equipment. Pre-calibration operations may include calibrating the dicing tool. By measuring the current state of the tool, such as tool height and blade wear, embodiments of this application can adjust the tool parameters to ensure that the dicing tool is in optimal working condition. This process typically uses high-precision sensors and measuring equipment, combined with automated control algorithms, to ensure the accuracy and reliability of the tool parameters.

[0177] In addition, pre-calibration can also involve calibrating the sample stage. Calibrating the flatness, positioning accuracy, and motion control system of the sample stage ensures the stability of the sample during cutting. By calibrating the sample stage, it is ensured that the sample maintains a precise position and height during cutting, avoiding cutting errors caused by minute displacements or vibrations of the sample stage.

[0178] In some more specific embodiments, the calibration of the optical system of the wafer dicing machine can also be part of the pre-calibration process. By adjusting the microscope's focal length, field of view, and image capture parameters, it is ensured that the cutting position and path of the sample can be clearly observed during the dicing process. This step is particularly important for high-precision wafer dicing because it directly affects the operator's or automated system's ability to monitor and adjust the dicing process.

[0179] Finally, pre-calibration can also include compensation for environmental parameters, such as the measurement and adjustment of temperature and humidity. Environmental factors can affect the wafer dicing machine and the sample, leading to minor deviations during the dicing process. By calibrating and compensating for these environmental parameters, the stability and accuracy of the dicing process can be further improved.

[0180] It should be understood that by performing these pre-calibration operations, the wafer dicing machine can reach a known and optimal working state before each dicing task begins, significantly reducing dicing errors caused by inconsistent or unstable equipment conditions. This step not only improves dicing quality and efficiency but also reduces equipment maintenance costs and operator workload, providing a solid technical guarantee for achieving automated and intelligent wafer dicing.

[0181] Reference Figure 7 According to some embodiments of this application, the wafer dicing machine may further include a worktable and a height sensor. Step S102 performs a pre-calibration operation on the wafer dicing machine to adjust it to a pre-operation state, which may include:

[0182] Step S701: Place the target sample in the working area of ​​the worktable and perform workpiece vacuum treatment on the working area to fix the target sample.

[0183] Step S702: Perform height measurement on the surface of the workbench based on the height sensor to obtain height calibration data;

[0184] Step S703: Based on the height measurement calibration data, control the wafer dicing machine to perform height measurement calibration processing to adjust the wafer dicing machine to the pre-operation state.

[0185] In some embodiments, step S701 involves placing the target sample in the working area of ​​the worktable and performing workpiece vacuum treatment on the working area to fix the target sample.

[0186] It should be noted that the target sample is placed within the working area of ​​the worktable. As the platform supporting the cutting operation, the stability and precision of the worktable are crucial to the entire cutting process. To ensure the sample remains stable during cutting, this embodiment of the application performs a workpiece vacuum treatment on the working area. This process creates a negative pressure environment by evacuating the air from under the worktable, thereby firmly adhering the target sample to the worktable. This fixing method not only prevents minute displacement of the sample during cutting but also effectively reduces cutting errors caused by sample movement, providing the necessary stability for high-precision cutting.

[0187] In some embodiments, step S702 involves measuring the height of the workbench surface using a height sensor to obtain height calibration data.

[0188] It should be noted that a height sensor is used to measure the height of the worktable surface to obtain height calibration data. The height sensor provides crucial data support for subsequent tool height adjustments by accurately measuring the height difference between the worktable surface and the cutting edge. This process typically involves multiple measurements and data recordings to ensure the accuracy and reliability of the height measurement data. The accuracy of the height measurement directly affects the accuracy of the relative position between the cutting tool and the sample surface, thus impacting cutting quality and equipment operating efficiency.

[0189] According to some embodiments of this application, step S702, which involves measuring the height of the workbench surface using a height sensor to obtain height calibration data, may include:

[0190] Retrieve the height measurement calibration prompt instruction from the preset operation procedure database;

[0191] The height measurement prompts the user to control the height sensor to measure the height of the workbench surface and obtain height calibration data.

[0192] In some embodiments of this application, step S702, which involves measuring the height of the workbench surface using a height sensor, is an intelligent and clearly guided operational process. In this process, height calibration prompts are first retrieved from a pre-set operational procedure database. This database is carefully designed and pre-configured, storing various prompts related to height calibration. These prompts act like an experienced operational guide, providing standardized guidance for subsequent height measurement operations. The purpose of retrieving these prompts is to ensure that each step of the height measurement operation conforms to established specifications and requirements, avoiding inconsistencies or errors caused by human factors, thereby improving the accuracy and reliability of the height measurement data.

[0193] Next, this embodiment of the application can execute height measurement calibration prompts based on the retrieved height measurement prompt instructions. The core of this operation lies in guiding the target user to correctly control the height sensor to measure the height of the workbench surface through clear prompts. The prompts can be presented in various forms, such as displaying detailed operation steps on the software interface, animated demonstrations, or voice prompts, ensuring that the user clearly understands each action required. This prompting mechanism not only lowers the operational threshold, enabling even inexperienced users to accurately complete height measurement operations, but also minimizes measurement errors caused by improper operation, ensuring the quality of the height measurement calibration data.

[0194] Ultimately, guided by the height calibration prompts, the user can accurately control the height sensor to complete the height calibration operation on the worktable surface, thereby obtaining height calibration data. This height calibration data is crucial for the entire intelligent operation process of the precision wafer dicing machine. It forms the basis for a series of subsequent precise operations, such as setting the blade height and controlling the cutting depth. Only based on accurate height calibration data can the blade maintain the appropriate distance and angle with the wafer during the cutting process, achieving high-precision cutting operations and thus improving production efficiency and product quality.

[0195] In some embodiments, step S703 involves controlling the wafer dicing machine to perform height calibration based on height calibration data, so as to adjust the wafer dicing machine to a pre-operation state.

[0196] It should be noted that, based on the height measurement calibration data, this embodiment of the application performs height measurement calibration on the wafer dicing machine. This step, by adjusting the height of the cutting tool and the vertical position of the worktable, ensures that the distance between the cutting tool and the sample surface reaches the preset optimal cutting position. Height measurement calibration is the core step in the entire pre-calibration operation; it converts the data acquired by the height sensor into actual equipment adjustment actions, bringing the wafer dicing machine into a known and stable state. In this state, the equipment can guarantee the accurate relative position of the cutting tool and the sample during the cutting process, thereby achieving efficient and precise cutting operations.

[0197] Through the above steps, the pre-calibration operation of the wafer dicing machine in this embodiment not only improves the stability and cutting accuracy of the equipment, but also reduces potential problems caused by unstable equipment status or incorrect parameter settings. This standardized pre-calibration process provides reliable technical support for subsequent cutting tasks, reflecting the intelligence and automation level of modern precision machining equipment.

[0198] According to some embodiments of this application, controlling a wafer dicing machine to perform height calibration based on height measurement calibration data to adjust the wafer dicing machine to a pre-operation state may include:

[0199] Retrieve the height measurement calibration prompt instruction from the preset operation procedure database;

[0200] The system executes height calibration prompts based on height measurement prompts, instructing the target user to control the wafer dicing machine to perform height calibration based on the height calibration data, so as to adjust the wafer dicing machine to the pre-operation state.

[0201] In some embodiments of this application, the process of controlling a wafer dicing machine to perform height calibration based on height calibration data is an intelligent and clearly guided operation. The primary task of this process is to retrieve height calibration prompts from a pre-set operation procedure database. This database is carefully designed and pre-configured, storing various prompts related to height calibration. These prompts act like an experienced operation guide, providing standardized guidance for subsequent height calibration. The purpose of retrieving these prompts is to ensure that each step of the height calibration conforms to established specifications and requirements, avoiding inconsistencies or errors caused by human factors, thereby improving the accuracy and reliability of the height measurement data and laying the foundation for precise adjustment of the wafer dicing machine.

[0202] Next, this embodiment of the application will execute a height calibration prompt operation based on the retrieved height measurement prompt instruction. The core of this operation lies in guiding the target user to accurately control the wafer dicing machine for height calibration based on the height calibration data through clear prompts. The prompt operation can be presented in various forms, such as displaying detailed operation steps on the software interface, animated demonstrations, or through voice prompts, ensuring that the user can clearly understand the actions required for each step. This prompt mechanism not only lowers the operational threshold, enabling even inexperienced users to accurately complete the height calibration operation, but also minimizes measurement errors caused by improper operation, ensuring the quality of the height calibration data.

[0203] Ultimately, guided by the height calibration prompts, the target user can accurately control the wafer dicing machine to complete the height calibration process based on the height calibration data, adjusting the wafer dicing machine to the pre-operation state. This process is crucial for ensuring the cutting accuracy and stability of the wafer dicing machine. By adjusting the wafer dicing machine to the pre-operation state, the relative position between the blade and the wafer can be guaranteed to be accurate during the actual cutting process, thereby achieving high-precision cutting operations. This not only helps improve production efficiency and product quality but also extends the service life of the equipment and reduces equipment maintenance costs. This entire process demonstrates the innovation and advantages of this application in intelligent operation and precise measurement, bringing a higher degree of automation and reliability to the operation of precision wafer dicing machines.

[0204] In some embodiments, step S103 determines the cutting operation settings parameters for the wafer dicing machine based on tool setting information, target sample parameters, and cutting control parameters.

[0205] It should be noted that determining the cutting operation settings for the wafer dicing machine based on tool setting information, target sample parameters, and cutting control parameters is the core step in achieving efficient and precise cutting. It should be understood that the embodiments of this application, through comprehensive analysis of tool setting information, target sample parameters, and cutting control parameters, can determine the optimal cutting operation settings for the wafer dicing machine. This step may involve complex algorithms and logical judgments to ensure that the cutting parameters meet the specific requirements of the target sample while adapting to the performance characteristics of the cutting tool. This step not only helps improve the accuracy and efficiency of cutting but also reduces equipment wear and sample damage caused by improper parameter settings, providing crucial technical support for achieving automated and intelligent wafer dicing.

[0206] In some embodiments, this step may also involve referencing historical data and empirical parameters. By analyzing successful cases of similar previous cutting tasks, current cutting parameter settings can be further optimized to improve cutting quality and efficiency. This data-driven optimization method can continuously improve the cutting process and adapt to differences in different batches of samples and cutting tools.

[0207] Reference Figure 8 According to some embodiments of this application, step S103, based on tool setting information, target sample parameters, and cutting control parameters, determines the cutting operation setting parameters for the wafer dicing machine, which may include:

[0208] Step S801: Determine the sample size information of the target sample based on the target sample parameters;

[0209] Step S802: Based on the cutting control parameters, determine the cutting type information, blade height parameters, feed speed information, and cutting step information;

[0210] Step S803: Based on sample size information, cutting type information, blade height parameters, feed speed information, and cutting step information, determine the cutting operation settings parameters for the wafer dicing machine.

[0211] In some embodiments, step S801 involves determining the sample size information of the target sample based on the target sample parameters;

[0212] It should be noted that sample size information is one of the fundamental parameters of the cutting operation, directly affecting the planning of the cutting path, the calculation of the number of cuts, and the range of motion of the cutting tool. By accurately identifying the sample size, the embodiments of this application can ensure that the cutting operation is carried out within the physical boundaries of the sample, avoiding cutting errors or equipment damage caused by exceeding size limits. Sample size information typically includes key dimensions such as the sample's length, width, and thickness, which can be obtained through user input, database queries, or automatic measurement.

[0213] In some embodiments, step S802 involves determining cutting type information, blade height parameters, feed speed information, and cutting step information based on cutting control parameters.

[0214] It's important to note that the cutting type information determines the overall cutting process mode, such as fully automatic or semi-automatic cutting, which directly affects the automation level of the wafer dicing machine and the degree of operator intervention. The blade height parameter is determined based on the sample thickness and coating type, ensuring the blade reaches the appropriate depth during cutting while avoiding sample damage due to over-cutting. The feed rate information determines the speed at which the tool moves along the cutting path, which needs to be adjusted according to the tool type, sample material, and cutting requirements to achieve efficient cutting. The cutting step information defines the feed amount for each cut, which relates to the continuity and accuracy of the cutting. An excessively large step may lead to incomplete cutting, while an excessively small step will reduce cutting efficiency.

[0215] In some embodiments, step S803 determines the cutting operation settings parameters for the wafer dicing machine based on sample size information, cutting type information, blade height parameters, feed speed information, and cutting step information.

[0216] It should be noted that the embodiments of this application comprehensively consider sample size information, cutting type information, blade height parameters, feed speed information, and cutting step information to determine the optimal cutting operation settings for the wafer dicing machine. This step integrates various parameters into a complete operation setting scheme through complex algorithms and logical judgments. For example, the embodiments of this application may calculate the maximum number of cuts based on sample size and cutting step, ensuring the feasibility of the cutting task. In addition, the embodiments of this application can also adjust the movement mode of the tool according to the cutting type to ensure the smoothness of fully automatic or semi-automatic cutting. The settings of blade height and feed speed need to be matched with the physical characteristics of the sample to achieve better cutting results.

[0217] In this embodiment, the wafer dicing machine can achieve precise and efficient operation in each dicing task. This not only improves dicing quality but also reduces equipment wear and sample damage caused by improper parameter settings. This intelligent parameter setting method provides strong technical support for the operation of the wafer dicing machine, ensuring its efficient application in precision processing fields such as semiconductor manufacturing.

[0218] According to some specific embodiments provided in this application, during the operation of the wafer dicing machine, when the dicing type is selected as semi-automatic dicing, the manual alignment interface will be entered. At this time, the automatic operation of the software will be temporarily stopped, and manual intervention is required to perform a series of key alignment and setting operations.

[0219] First, the operator needs to "flatten the sample." This step aims to ensure the sample is stable and has a flat surface on the worktable, providing a foundation for subsequent precise cutting. By manually adjusting the sample's position and angle, the operator can eliminate any tilting or unevenness that may occur during placement, ensuring stability and accuracy during the cutting process.

[0220] Next, the operator needs to adjust the image focus to ensure clear observation of sample details under the microscope. Specifically, click the "Switch Microscope" button to switch the microscope to low-power mode and locate the approximate position of the sample. Then, click the "Image Focus" option and use the "Small Stroke" function for autofocus. This process can be performed separately under low and high magnification to ensure a clear image at different magnifications. After focusing, click the "Confirm" button to return to the manual alignment interface. Clear image focus adjustment is crucial for accurately selecting the cutting start point and monitoring the cutting process.

[0221] Next, the operator needs to perform a sampling operation to determine two key points on the sample that need to be straightened along the horizontal line. First, align the center of the green crosshair of the baseline with the leftmost point of the flat edge and click the "Sample" button. The sampling interface will turn red, indicating that the first point has been successfully selected. Then, move the microscope field of view to align the center of the green crosshair of the baseline with the rightmost point of the flat edge and click the "Sample" button again. The sampling button will turn green, indicating that the second point has also been successfully selected. These two points provide the system with a reference for straightening the horizontal line, ensuring the straightness and accuracy of the cutting path.

[0222] Finally, the operator needs to select the specific cutting position. Typically, the cutting position is chosen 0.5-1mm down from the zeroed-out coordinates at the flat edge. After selecting the position, click the "Record" button to save the position information and confirm that this is cutting channel 1. After completing these settings, return to the cutting interface to prepare for the cutting operation. This step ensures the accuracy and consistency of the cutting starting position, providing a precise starting point for subsequent cutting tasks.

[0223] In some embodiments of the semi-automatic dicing mode of a wafer dicing machine, the operator needs to perform a series of manual adjustments and alignment operations during the dicing process to ensure the accuracy and quality of the dicing.

[0224] First, the operator clicks the "Single Step" button to enter the single-step cutting mode. In this mode, the operator can manually adjust the position and width of the baseline to ensure that the blade width meets the cutting requirements. The purpose of this step is to ensure that the blade width of the cutting tool is precisely matched with the actual cutting path position through fine adjustment of the baseline. The operator needs to carefully observe the image under the microscope and manually fine-tune the position of the sample stage or tool to align the baseline with the predetermined cutting path on the sample. After completing the adjustment, the operator clicks the "Baseline Correction" button, and the cutting parameters will be updated according to the adjusted baseline position in this embodiment. Then, the operator clicks the "Restart" button to continue the cutting operation, and the equipment will continue to perform the cutting task according to the updated parameters. When the cutting task of channel 1 is completed, the operator needs to prepare for the next cutting channel. At this time, the operator clicks the "Manual Alignment" button to enter the manual alignment interface.

[0225] Next, the current channel is switched from channel 1 to channel 2, and the wafer is automatically rotated 90° clockwise. This automatic rotation function ensures the correct cutting direction, simplifies the operation process, and reduces errors caused by manual intervention. In most cases, unless there are special requirements, operators do not need to re-take points, as the system will automatically adjust the cutting position to adapt to the new cutting direction.

[0226] To further confirm the accuracy of the cutting position, the operator needs to click the "Switch Microscope" button to switch the microscope to low-magnification mode, allowing for a wider field of view to locate the starting point of the sample cutting. Typically, the cutting position is selected 0.5-1 mm from the top of the wafer; this is a proven and reasonable position that ensures the integrity of the cut and the stability of the sample. After selecting the cutting position, the operator clicks the "Record" button. This embodiment of the application saves the current cutting position information and returns to the cutting interface, ready to begin the cutting task for the next channel.

[0227] Through these combined manual and automatic operating steps, the wafer dicing machine can achieve efficient and precise dicing operations in semi-automatic mode. Operator intervention ensures the flexibility and adaptability of the dicing process, while the equipment's automation functions improve operational efficiency and accuracy, reducing human error. These steps together constitute the important operating procedures of the wafer dicing machine in the field of precision machining, providing a reliable dicing solution for high-precision industries such as semiconductor manufacturing. It should be understood that the dicing in the embodiments of this application is not limited to the examples described above.

[0228] In some embodiments, step S104 involves adjusting the wafer dicing machine from a pre-operation state to an operation state, and performing a dicing operation on the target sample according to the dicing operation settings parameters to obtain the target wafer.

[0229] It should be noted that adjusting the wafer dicing machine from pre-operation mode to operation mode, and performing the dicing operation on the target sample according to the dicing operation settings, is the key execution stage for achieving the wafer dicing goal. This step marks the transition of the wafer dicing machine from the preparation stage to the actual operation stage, and all parameters acquired and calibration operations performed in the early stages will be applied and verified in this step.

[0230] In some embodiments, adjusting the wafer dicing machine from a pre-operation state to an operation state may include starting the spindle of the device, setting its speed to match the spindle speed specified in the dicing operation settings parameters, and adjusting the feed system to ensure that the tool can cut at a predetermined feed rate and step distance. Additionally, the cooling system can also be activated according to the dicing operation settings parameters, providing coolant at an appropriate flow rate to ensure that the temperature of the cutting area is controlled within an ideal range. Once everything is ready, the wafer dicing machine begins the dicing operation on the target sample according to the dicing operation settings parameters. During the dicing process, the tool will precisely cut according to the predetermined cutting path and depth, ensuring dicing quality and efficiency. Simultaneously, the wafer dicing machine's control system can monitor various parameters during the dicing process in real time, such as tool load, cutting speed, and sample position, ensuring the stability and accuracy of the entire dicing process.

[0231] It should be understood that step S104 is a crucial part of the wafer dicing machine control method of this application. By converting the tool setting information, target sample parameters, and dicing control parameters obtained in the early stage into actual dicing operations, the accuracy and efficiency of the dicing process are ensured.

[0232] Reference Figure 9 According to some embodiments of this application, step S104, which adjusts the wafer dicing machine from a pre-operation state to an operation state and performs a dicing operation on the target sample according to the dicing operation setting parameters to obtain the target wafer, may further include:

[0233] Step S901: During the cutting operation on the target sample, the data of the cutting machine equipment is collected in real time, and the tool setting information, target sample parameters and cutting control parameters that match the data of the cutting machine equipment are obtained.

[0234] Step S902: Based on the cutting machine equipment data, the tool setting information matched with the cutting machine equipment data, the target sample parameters, and the cutting control parameters, the operation process is analyzed to obtain operation process analysis data.

[0235] In step S901 of some embodiments, during the cutting operation on the target sample, the cutting machine equipment data is collected in real time, and the tool setting information, target sample parameters and cutting control parameters that match the cutting machine equipment data are obtained.

[0236] It should be noted that the embodiments of this application can collect cutting machine equipment data in real time during the cutting operation. This equipment data covers various key parameters in the cutting process, such as the real-time position of the tool, feed rate, spindle speed, cutting depth, and coolant flow rate. Furthermore, the embodiments of this application can also acquire tool setting information, target sample parameters, and cutting control parameters that match this equipment data. The real-time acquisition of this information provides a rich data foundation for subsequent analysis of the operation process.

[0237] In step S902 of some embodiments, the operation process is analyzed based on the cutting machine equipment data, the tool setting information matched with the cutting machine equipment data, the target sample parameters and the cutting control parameters to obtain operation process analysis data.

[0238] It should be noted that this application embodiment analyzes the work process based on the collected cutting machine equipment data, related tool setting information, target sample parameters, and cutting control parameters. The purpose of work process analysis is to dynamically monitor the progress and status of the cutting process by analyzing real-time data. This application embodiment compares the real-time collected data with the pre-set cutting operation setting parameters to verify whether the cutting operation is executed according to the predetermined plan. For example, this application embodiment checks whether the actual position of the tool is consistent with the planned cutting path, whether the feed speed is stable near the set value, and whether the cutting depth meets the requirements. The work process analysis data is not only used for real-time monitoring but also for predicting and preventing potential problems. For example, if this application embodiment detects that tool wear causes the cutting depth to gradually deviate from the set value, it can issue an early alarm to prompt the operator to replace or adjust the tool. Furthermore, this analysis data can also be used to optimize subsequent cutting tasks. Through the analysis of historical data, this application embodiment can identify key factors affecting cutting efficiency and quality and adjust the cutting operation setting parameters accordingly to achieve more efficient and precise cutting operations.

[0239] This application's embodiments enable the wafer dicing machine to achieve refined management and control of the dicing operation by acquiring equipment data in real time and analyzing the operation progress during the dicing process. This not only improves the accuracy of dicing and the reliability of the equipment but also provides data support for continuous improvement of the dicing process. This real-time monitoring and analysis capability is an important manifestation of the intelligence of modern precision machining equipment, providing high-quality production assurance for fields such as semiconductor manufacturing.

[0240] In some embodiments of this application, cutting machine equipment data is collected in real time, and tool setting information, target sample parameters, and cutting control parameters matching the cutting machine equipment data are obtained, ensuring a data foundation for subsequent analysis and application. Data acquisition can be divided into two modes: manual mode and automatic mode. In manual mode, operators need to manually extract data from system files. This method is typically used for reviewing historical data or in specific situations not yet covered by the automated system. Manual extraction requires operators to possess certain technical knowledge to ensure the accuracy and completeness of the extracted data. Automatic mode, on the other hand, is more efficient and convenient, extracting data directly from the initial input interface. With the development of automation technology, this method is becoming increasingly popular. Automatic extraction not only reduces human error but also enables real-time data acquisition, providing the possibility for real-time monitoring and rapid response.

[0241] It should be understood that during the operation and management of wafer dicing machines, the equipment's operation logs, process data, and blade files contain a wealth of information. However, this data is usually recorded in a simple and direct form, thus requiring no complex analysis. The key lies in automatically acquiring the critical parameters and classifying and mapping them according to certain rules to facilitate subsequent analysis and application. These critical parameters can include various information input by the equipment administrator and user before the experiment begins. Parameters input by the equipment administrator, such as blade type (hard or soft) and the blade exposure of hard blades, directly affect the selection of the dicing process and the efficiency of tool usage. Parameters input by the user cover detailed information ranging from file name, sample thickness, size, and type to film type, dicing type, spindle speed, blade height, feed rate, step distance, and number of dicings. These parameters collectively constitute the basic data framework of the dicing process, providing necessary information support for subsequent process analysis and equipment management.

[0242] In some embodiments of this application, following data acquisition, the extracted data is immediately parsed and classified. The purpose of this step is to transform the raw data into meaningful information and organize it according to a certain logic to facilitate subsequent analysis and presentation. The parsing process typically involves data format conversion, data cleaning to remove noise or erroneous data, and preliminary statistical analysis of the data. Classification involves categorizing the data into different types based on its attributes and uses, such as process parameters, equipment status, and consumable usage. Presenting the parsed and classified data in tabular form provides a more intuitive overview of the data, enabling operators and management to quickly obtain key information and make informed decisions.

[0243] Furthermore, the extracted and processed data can be used for several advanced applications, with key parameter analysis of processes and intelligent equipment management being two important directions. In key parameter analysis of processes, combining artificial intelligence models with in-depth data analysis can reveal hidden patterns and trends in the process. For example, through comprehensive analysis of parameters such as cutting speed, blade wear, and sample thickness, AI models can predict optimal cutting parameter settings, thereby improving cutting quality and efficiency. In addition, data analysis can help identify potential process problems, allowing for proactive optimization measures, reducing scrap rates, and saving costs.

[0244] In terms of intelligent equipment management, utilizing extracted data to achieve automated equipment management and maintenance is a significant trend in the current intelligent manufacturing industry. For example, through real-time monitoring of equipment operation data, this embodiment can automatically calculate the usage of consumables and trigger automatic billing or consumable replacement reminders accordingly. Taking tool management as an example, this embodiment can automatically calculate the replacement cycle based on the tool's usage time and wear level, and notify maintenance personnel when a threshold is reached. This intelligent management method not only improves equipment operating efficiency but also reduces unplanned downtime and extends equipment lifespan.

[0245] In summary, data acquisition, analysis, classification, and subsequent applications are crucial steps in achieving intelligent and efficient wafer dicing machines. These steps enable better utilization of data resources, improving the precision of production processes and the efficiency of equipment management.

[0246] In some specific embodiments of this application, the innovative application of a violation alarm mechanism in the operation of a wafer dicing machine provides important guarantees for the safe operation and efficient management of the equipment. This mechanism effectively prevents equipment damage or reduced dicing quality due to misoperation by setting prohibited buttons on the equipment's software interface for specific high-risk operations and triggering warnings and locking functions when a user accidentally touches the button. For example, when a user attempts to click the "Change Tool" button, this embodiment immediately displays a warning message "Unauthorized Tool Change Prohibited," and disables the mouse and touchscreen functions, ensuring that the screen can only be unlocked and operation continued after an equipment administrator arrives on-site and confirms safety. This measure avoids potential damage to the tools and equipment caused by improper operation by non-professionals.

[0247] Similarly, when a user attempts to click the "Cut Backwards" button, this embodiment of the application will display a warning "Cut Backwards Prohibited," and will also disable mouse and touchscreen functions, awaiting administrator intervention. This immediate warning and locking mechanism not only protects the device but also enforces operating procedures, ensuring the orderliness and safety of the cutting process.

[0248] Furthermore, the violation alarm mechanism also incorporates the real-time screen recording and screenshot functions of the monitoring software. When a user logs into the reservation system and begins operation, the monitoring software continuously records the screen and monitors the device's software interface. Once an alarm event is detected, the software automatically triggers a screenshot and saves a 5-second video clip before and after the alarm to a designated alarm folder, with the file name formatted as "time + alarm content". This recording function not only helps in tracing and analyzing the cause of the alarm afterward but also provides detailed data support for equipment management and maintenance.

[0249] In manual mode, although the parameter limitation rules are consistent with those specified in the document, the monitoring software must identify, acquire, and record process parameters in various software interfaces of the device because it is impossible to input all parameters at once on the main interface. In this case, if a process parameter is found to be inconsistent with the limitation rules, this embodiment will immediately issue an alarm. For example, when the blade height is set below the minimum allowable value, this embodiment will pop up a warning window "Blade height setting exceeds limit, please modify," and disable the mouse and keyboard for 30 seconds. If the user enters an incorrect blade height again, the warning window will pop up again, and the disabling time will be extended to 1 minute. If the third input is still incorrect, this embodiment will permanently disable the mouse and keyboard, forcing the user to contact the equipment administrator for processing.

[0250] This tiered warning and locking mechanism not only effectively prevents equipment damage or dicing failures caused by incorrect parameter settings, but also educates users to comply with operating procedures through escalating penalties. Through these innovative violation alarm mechanisms, wafer dicing equipment can significantly improve equipment reliability and management efficiency while ensuring operational safety, providing a solid guarantee for precision processing fields such as semiconductor manufacturing.

[0251] According to some embodiments of this application, step S104, which involves performing a cutting operation on the target sample according to the cutting operation settings parameters, may include:

[0252] During the cutting operation, a job monitoring operation is performed.

[0253] In response to the capture of non-compliant work actions by the work monitoring operation, the corresponding non-compliant action is determined based on the type of non-compliant work action;

[0254] After determining the violation handling action that matches the violation operation type, the violation handling action is executed to handle the violation operation.

[0255] In some embodiments of this application, the process of performing a cutting operation on a target sample according to the cutting operation settings is an intelligent operation with a strict monitoring and processing mechanism. The primary task of this process is to simultaneously perform operation monitoring during the cutting operation. The purpose of this operation monitoring is to monitor various actions and parameters during the cutting process in real time, ensuring that the cutting operation is strictly performed according to the preset cutting operation settings, thereby guaranteeing cutting quality and equipment safety. The monitoring system can continuously collect and analyze data during the cutting process, such as cutting speed, blade height, and sample stress, and compare it with preset parameter ranges to promptly detect any possible abnormalities or violations.

[0256] If the operation monitoring system detects a violation, this embodiment can immediately activate the corresponding processing mechanism. The core of this mechanism lies in determining the appropriate violation handling action based on the type of violation. Different types of violations may correspond to different levels of risk and impact, thus requiring different handling measures. For example, for minor parameter deviations, this embodiment may issue a warning and prompt the operator to make adjustments; while for serious violations, such as incorrect blade position or cutting path deviation, this embodiment may immediately suspend the cutting operation to prevent damage to the sample or equipment. This matching mechanism relies on predefined violation types and corresponding handling strategies, which are typically developed based on extensive experimental data and expertise to ensure the most appropriate handling measures are taken in various situations.

[0257] After identifying the violation handling operation matching the type of violation, this embodiment can automatically execute the corresponding violation handling operation to handle the violation. This process demonstrates the advantages of intelligent operation, namely, the ability to quickly respond to and correct abnormal situations, thereby minimizing losses caused by violations. Violation handling operations may include automatically adjusting cutting parameters, prompting operators to intervene manually, and recording violation events for subsequent analysis. By executing these handling operations, this embodiment can not only resolve problems promptly but also provide a basis for future operations, thereby continuously optimizing the cutting operation process and parameter settings. This entire process ensures high precision, high efficiency, and high safety in the cutting operation, and is a key link in realizing intelligent precision wafer cutting.

[0258] According to some embodiments of this application, the job monitoring operation involves capturing the operation monitoring screen of the corresponding operation terminal for the dicing operation. The wafer dicing machine control method of this application embodiment may further include:

[0259] In response to an alarm notification captured on the operation monitoring screen, determine the current time point;

[0260] Determine the preceding and following time intervals of the current time node;

[0261] Based on the preceding and subsequent time intervals, the target monitoring screen is extracted from the operation monitoring screen and identified as alarm association record information.

[0262] Compliance analysis is performed based on alarm correlation record information to obtain alarm correlation analysis results.

[0263] In some embodiments of this application, the operation monitoring system captures the operation monitoring screen of the corresponding operation terminal to achieve real-time monitoring and recording of the cutting process. A key aspect of this process is the ability to quickly determine the current time point in response to an alarm notification captured on the operation monitoring screen. Determining this time point is crucial because it marks the specific moment the abnormal event occurred, providing a precise time reference for subsequent analysis and processing. Through accurate time point positioning, embodiments of this application can quickly switch to the corresponding monitoring screen segment for detailed review of the abnormal situation.

[0264] Based on the current time point, this embodiment further determines the preceding and following time intervals. The purpose of this operation is to define a reasonable range on the timeline to comprehensively capture the operational processes related to the alarm notification. The preceding time interval allows the system to rewind to a period before the anomaly occurred to examine operational behaviors or parameter changes that might have led to the anomaly; while the following time interval is used to observe the system's response and the operators' countermeasures after the anomaly occurred. In this way, this embodiment can completely reconstruct the entire sequence of events surrounding the alarm from a temporal perspective, providing sufficient data support for subsequent analysis.

[0265] Next, this embodiment of the application extracts the target monitoring screen from the operation monitoring screen based on the preceding and following time intervals, and identifies this part of the screen as alarm-related record information. This process involves filtering and extracting a large amount of monitoring data, aiming to accurately locate the part directly related to the alarm event from the massive amount of monitoring screens. These target monitoring screens not only include the screen at the moment the anomaly occurs, but also the operation process before and after the anomaly, thus forming a complete event record. This alarm-related record information will serve as an important basis for subsequent compliance analysis. Through detailed review of these screens, it is possible to gain a deeper understanding of whether there were any violations or operational errors during the cutting operation.

[0266] Finally, this embodiment of the application performs compliance analysis based on alarm correlation record information to obtain alarm correlation analysis results. This analysis process typically involves comparing preset operating procedures with actual operating behaviors to check for deviations or violations. The results of the compliance analysis will provide important references for equipment management, operation training, and process optimization. Through in-depth analysis of alarm events, this embodiment of the application can identify potential operational risks and improvement points, thereby further improving the safety, accuracy, and efficiency of cutting operations. This entire process demonstrates the key role of intelligent monitoring systems in ensuring the quality of cutting operations and equipment safety, and also showcases their significant value in continuously improving and optimizing operating procedures.

[0267] According to some embodiments of this application, the wafer dicing machine control method of this application may further include:

[0268] During the cutting operation, the current operation process is determined in real time;

[0269] Based on the current operation process, a comparison is made with the preset operation specification benchmark to determine the currently disabled controls that match the current operation process; wherein, the operation specification benchmark records multiple preset operation processes and the types of disabled controls that match each preset operation process;

[0270] On the corresponding operation terminal for the cutting operation, disable and lock the currently disabled control.

[0271] In some embodiments of this application, the wafer dicing machine control method further enhances the safety, standardization, and efficiency of dicing operations through an intelligent monitoring and operation process management mechanism. A key aspect of this method is the real-time determination of the current operation process during dicing. By real-time monitoring and analysis of various operational actions and parameter settings on the operating terminal, embodiments of this application can accurately identify the specific dicing operation stage currently in progress. This real-time monitoring mechanism utilizes advanced sensor technology and data analysis algorithms, enabling the judgment and classification of the operation process within milliseconds, thereby ensuring that the system promptly grasps the dynamic changes in the dicing operation.

[0272] Based on the current operating procedure, this embodiment compares the current procedure with a preset operating standard benchmark to determine the currently disabled controls that match the current operating procedure. The operating standard benchmark is a carefully designed and maintained database that records multiple preset operating procedures and the types of disabled controls that match each preset operating procedure. These disabled control types are determined based on extensive experimental data, equipment operating procedures, and safety standards, with the aim of preventing the execution of control operations that may cause risks or errors under specific operating procedures. By comparing the current operating procedure with the operating standard benchmark, this embodiment can quickly identify controls that should be disabled at the current stage, such as certain function buttons that may cause cutting path deviation or tool damage.

[0273] On the corresponding operating terminal for the cutting operation, this embodiment of the application performs a disable / lock operation on currently disabled controls. This operation is implemented through interface interaction technology, such as graying out, locking, or hiding the corresponding buttons, thereby preventing operators from accidentally touching or intentionally clicking these controls. The disable / lock operation not only improves the safety of the cutting process but also effectively reduces equipment failures and production delays caused by operational errors. In this way, this embodiment of the application ensures that the cutting operation strictly follows preset operating specifications, thereby improving cutting quality and production efficiency while reducing equipment maintenance costs and operational difficulty. This intelligent control mechanism fully demonstrates the innovation and advantages of this application in improving the intelligence level of wafer dicing machine operation, providing reliable technical support for precision processing industries such as semiconductor manufacturing.

[0274] According to some embodiments of this application, after performing a cutting operation on the target sample according to the cutting operation settings parameters to obtain the target wafer, the process may further include:

[0275] Obtain the parameter parsing requirements and the actual cutting parameters collected for the cutting operation;

[0276] Based on the parameter parsing requirements, the actual collected parameters for cutting are parsed to obtain the required parsing result data.

[0277] In some embodiments of this application, after successfully obtaining the target wafer by performing a cutting operation on the target sample according to the cutting operation settings, its functionality is further expanded to achieve in-depth utilization and analysis of the cutting process data. This process first involves obtaining parameter parsing requirements and the actual cutting parameters collected corresponding to the cutting operation. Parameter parsing requirements typically come from multiple sources. For example, process engineers may need to understand specific parameters in the cutting process to optimize the process flow, equipment maintenance personnel may focus on equipment performance-related data to assess equipment status, or quality control departments may need to collect data for product quality traceability and analysis. These requirements can be obtained through user input on the operating terminal. Embodiments of this application provide corresponding parameter parsing options and configuration interfaces based on the user's role and permissions.

[0278] Next, based on these parameter analysis requirements, this embodiment analyzes the actual cutting parameters to obtain the analysis results data. This analysis process may involve various data processing techniques, such as data cleaning, feature extraction, and pattern recognition, to extract valuable information from the original actual cutting parameters. For example, through comprehensive analysis of parameters such as cutting speed, blade wear, and sample stress, this embodiment can identify key factors affecting cutting quality and generate corresponding reports or visualizations. These analysis results data not only help users better understand various phenomena and problems in the cutting process but also provide data support for subsequent process improvement, equipment maintenance, and quality control.

[0279] Furthermore, this parameter analysis process is closely linked to the entire intelligent operation process of the wafer dicing machine, forming a closed-loop data utilization model. Before the dicing operation is executed, this embodiment ensures the standardization and safety of the operation through parameter input and logical judgment; during the dicing process, real-time monitoring and violation handling ensure the smooth progress of the operation; and after the dicing is completed, by analyzing the actual parameters collected during the dicing, this embodiment further mines the value of the data, providing users with specific and actionable feedback and suggestions. This end-to-end intelligent management, from operation to monitoring to data analysis, improves the efficiency and processing quality of the wafer dicing machine.

[0280] According to some embodiments of this application, parsing the actual collected parameters for cutting based on parameter parsing requirements to obtain requirement parsing result data may include:

[0281] In response to the parameter parsing requirement, the consumable cost parsing requirement is to obtain the unit price of consumables corresponding to various preset consumable types;

[0282] Based on the actual cutting parameters collected, consumable parameters are extracted to obtain actual consumable parameters, such as ultrapure water usage time and blade wear.

[0283] Cost calculations are performed based on actual consumable parameters and unit prices to obtain demand analysis results data that match the needs of consumable cost analysis.

[0284] In some embodiments of this application, the process of analyzing actual cutting parameters based on parameter analysis requirements, particularly regarding consumable cost analysis requirements, embodies a refined resource management and cost control strategy. When the parameter analysis requirement is determined to be a consumable cost analysis requirement, embodiments of this application first respond to this requirement and then obtain the unit price of consumables corresponding to various preset consumable types. This step involves obtaining accurate consumable price information from a preset database or external resource management system. These consumable types may include cutting blades, film materials, coolant, etc.

[0285] Next, this embodiment of the application extracts consumable parameters based on the actual cutting parameters collected to obtain the actual consumable parameters. This process relies on data collected in real time during the cutting operation, such as blade usage time, film coverage area, and coolant consumption. By extracting and analyzing this actual operational data, this embodiment of the application can accurately calculate the actual usage of each consumable in a specific cutting task. This consumable parameter extraction mechanism not only requires high-precision data acquisition equipment but also intelligent data processing algorithms to identify and separate data directly related to consumable usage, thereby providing a solid data foundation for subsequent cost calculations.

[0286] Finally, this embodiment of the application performs cost calculations based on actual consumable parameters and the obtained unit price of consumables, obtaining demand analysis result data that matches the consumable cost analysis requirements. This cost calculation process typically employs a specific calculation model, multiplying the actual usage of each consumable by its corresponding unit price, and summing the results to obtain the consumable cost for the entire cutting task.

[0287] Reference Figure 10 , Figure 10 This illustration shows the hardware structure of an electronic device according to another embodiment. The electronic device may include:

[0288] The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0289] The memory 1002 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1002 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001 to execute the wafer dicing machine control method of the embodiments of this application.

[0290] Input / output interface 1003 is used to implement information input and output;

[0291] The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0292] Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004);

[0293] The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0294] This application also provides a computer program product, which may include a computer program. The processor of a computer device reads and executes the computer program, causing the computer device to perform the wafer dicing machine control method described above.

[0295] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in this disclosure and the foregoing drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “may include” and “comprising,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatuses.

[0296] It should be understood that in this disclosure, "at least one item" means one or more, and "more than one" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, which can include any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0297] It should be understood that in the description of the embodiments of this application, "multiple" means two or more, "greater than", "less than", "exceeding" etc. are understood to exclude the number itself, while "above", "below", "within" etc. are understood to include the number itself.

[0298] In the several embodiments provided in this disclosure, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0299] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0300] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and may include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this disclosure. The aforementioned storage medium may include: a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and other media capable of storing program code.

[0301] It should also be understood that the various implementation methods provided in this application can be combined arbitrarily to achieve different technical effects.

[0302] The above is a detailed description of the embodiments of this disclosure. However, this disclosure is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this disclosure. All such equivalent modifications or substitutions can be included within the scope defined by the claims of this disclosure.

Claims

1. A control method for a wafer dicing machine, characterized in that, The method is applied to the control of a wafer dicing machine, which includes dicing tools, a worktable, and a height sensor. The method includes: Obtain tool setting information matching the cutting tool, target sample parameters matching the target sample, and cutting control parameters; The target sample is placed in the working area of ​​the worktable, and the working area is subjected to workpiece vacuum treatment to fix the target sample. The height of the workbench surface is measured using the height sensor to obtain height calibration data. Based on the height measurement calibration data, the wafer dicing machine is controlled to perform height measurement calibration to adjust the wafer dicing machine to a pre-operation state; Based on the target sample parameters, determine the sample size information of the target sample; Based on the cutting control parameters, the cutting type information, blade height parameters, feed speed information, and cutting step information are determined. Based on the sample size information, the cutting type information, the blade height parameter, the feed speed information, and the cutting step information, the cutting operation setting parameters are determined for the wafer dicing machine; The wafer dicing machine is adjusted from the pre-operation state to the operation state, and the dicing operation is performed on the target sample according to the dicing operation setting parameters to obtain the target wafer.

2. The method according to claim 1, characterized in that, The process of acquiring tool setting information matching the cutting tool, target sample parameters matching the target sample, and cutting control parameters includes: Acquire tool setting input information, target sample input parameters, and cutting control input parameters; Based on the tool setting input information, the tool type corresponding to the cutting tool is determined; Based on the tool type, data compliance checks are performed on the target sample input parameters and the cutting control input parameters to obtain data compliance check results; If the data compliance test result meets the preset compliance test conditions, the tool setting information is determined based on the tool setting input information, the target sample parameters are determined based on the target sample input parameters, and the cutting control parameters are determined based on the cutting control input parameters.

3. The method according to claim 2, characterized in that, The step of performing data compliance checks on the target sample input parameters and the cutting control input parameters based on the tool type, and obtaining data compliance check results, includes: Based on the target sample input parameters, the sample type is confirmed to obtain sample type input information; Based on the cutting control input parameters, the feed rate is confirmed to obtain the feed rate input information; Based on the tool type, the sample type input information is used for compliance testing to obtain the sample type test result; Based on the tool type, the feed rate input information is checked for compliance, and the feed rate detection result is obtained; Based on the sample type detection results and the feed rate detection results, the data compliance detection results are generated.

4. The method according to claim 3, characterized in that, The compliance check based on the tool type and the sample type input information to obtain the sample type check result includes: In response to the fact that the tool type is a hard tool type, a compliance check is performed on the sample type input information. If the sample type input information includes sample types other than silicon wafers or silicon dioxide wafers, sample type abnormality information is generated as the sample type detection result. The compliance check of the feed rate input information based on the tool type, to obtain the feed rate detection result, includes: In response to the fact that the tool type is a hard tool, a compliance check is performed on the feed rate input information. If the feed rate input information is greater than a first preset speed value, feed rate abnormality information is generated as the feed rate detection result.

5. The method according to claim 3, characterized in that, The compliance check based on the tool type and the sample type input information to obtain the sample type check result includes: In response to the fact that the tool type is a soft tool type, a compliance check is performed on the sample type input information. If the sample type input information includes sample types other than quartz plates, glass plates, or sapphire plates, sample type abnormality information is generated as the sample type detection result. The compliance check of the feed rate input information based on the tool type, to obtain the feed rate detection result, includes: In response to the fact that the tool type is a soft tool, a compliance check is performed on the feed rate input information. If the feed rate input information is greater than a second preset speed value, feed rate abnormality information is generated as the feed rate detection result.

6. The method according to claim 1, characterized in that, The step of measuring the height of the workbench surface based on the height sensor to obtain height calibration data includes: Retrieve the height measurement calibration prompt instruction from the preset operation procedure database; The height calibration prompt operation is executed according to the height calibration prompt instruction, so as to instruct the target user to control the height sensor to perform height measurement operation on the surface of the workbench and obtain the height calibration data.

7. The method according to claim 1, characterized in that, The step of controlling the wafer dicing machine to perform height calibration based on the height measurement calibration data, in order to adjust the wafer dicing machine to a pre-operation state, includes: Retrieve the height measurement calibration prompt instruction from the preset operation procedure database; The height calibration prompt instruction is executed to instruct the target user to control the wafer dicing machine to perform height calibration based on the height calibration data, so as to adjust the wafer dicing machine to the pre-operation state.

8. The method according to claim 1, characterized in that, The step of adjusting the wafer dicing machine from the pre-operation state to the operation state, and performing a dicing operation on the target sample according to the dicing operation setting parameters to obtain the target wafer, includes: During the cutting operation on the target sample, the cutting machine equipment data is collected in real time, and the tool setting information, target sample parameters and cutting control parameters that match the cutting machine equipment data are obtained. After adjusting the wafer dicing machine from the pre-operation state to the operation state and performing a dicing operation on the target sample according to the dicing operation settings parameters to obtain the target wafer, the process further includes: Based on the cutting machine equipment data, the tool setting information matched with the cutting machine equipment data, the target sample parameters, and the cutting control parameters, the operation process is analyzed to obtain operation process analysis data.

9. The method according to claim 1, characterized in that, The step of performing a cutting operation on the target sample according to the cutting operation settings includes: During the execution of the cutting operation, a job monitoring operation is performed; In response to the capture of a violation operation by the operation monitoring, a matching violation handling operation is determined based on the violation operation type of the violation operation; After determining the violation handling operation that matches the violation operation type, the violation handling operation is executed to handle the violation operation.

10. The method according to claim 9, characterized in that, The operation monitoring operation involves capturing the operation monitoring screen of the corresponding operation terminal for the cutting operation. The method further includes: In response to an alarm notification captured on the operation monitoring screen, determine the current time point; Determine the preceding and following time intervals of the current time node; Based on the preceding time interval and the following time interval, the target monitoring screen is extracted from the operation monitoring screen, and the target monitoring screen is identified as alarm association record information; Compliance analysis is performed based on the alarm association record information to obtain alarm association analysis results.

11. The method according to claim 1, characterized in that, The method further includes: During the execution of the cutting operation, the current operation process is determined in real time; Based on the current operation process, a comparison is performed with a preset operation specification benchmark to determine the currently disabled control that matches the current operation process; wherein, the operation specification benchmark records multiple preset operation processes and the type of disabled control that matches each preset operation process; On the operation terminal corresponding to the cutting operation, a disable / lock operation is performed on the currently disabled control.

12. The method according to claim 1, characterized in that, After performing a cutting operation on the target sample according to the cutting operation settings to obtain the target wafer, the process further includes: Obtain the parameter parsing requirements and the actual cutting parameters collected for the cutting operation; Based on the parameter analysis requirements, the actual collected parameters for the cutting are analyzed to obtain the analysis result data.

13. The method according to claim 12, characterized in that, The step of parsing the actual collected parameters for cutting based on the parameter parsing requirements to obtain the requirement parsing result data includes: In response to the parameter parsing requirement being a consumable cost parsing requirement, the unit price of consumables corresponding to various preset consumable types is obtained; Based on the actual cutting parameters collected, consumable parameters are extracted to obtain the actual consumable parameters; Cost calculation is performed based on the actual consumable parameters and the unit price of the consumables to obtain the demand analysis result data that matches the demand analysis requirements for the consumables cost.

14. An electronic device, characterized in that, include: The device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the wafer dicing machine control method as described in any one of claims 1 to 13.

15. A computer-readable storage medium, characterized in that, The storage medium stores a program that is executed by a processor to implement the wafer dicing machine control method as described in any one of claims 1 to 13.

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