Chopper track and cutter head size regulation and control method and system based on lead impedance feedback and medium

Abstract

The invention relates to a chopper track and cutter head size regulation and control method and system based on lead impedance feedback and a medium, and belongs to the technical field of microelectronic packaging, and the method comprises the steps: comparing an impedance parameter of a target lead with a preset impedance parameter range to obtain an impedance deviation of the target lead; if the impedance deviation exceeds the preset threshold value, repeating the following steps until the impedance deviation is within the preset threshold value range; inputting the impedance deviation into an impedance-track mapping model, and outputting a chopper track adjustment instruction set; based on the impedance change trend of the target lead and the chopper track adjustment instruction, evaluating the size of the cutter head and switching the cutter head into a cutter head with an adaptive size; comparing the impedance parameter with a preset impedance parameter range to obtain an impedance deviation of the target lead, and judging whether the impedance deviation is within a preset threshold range or not; and if the impedance deviation is within the preset threshold range, adjusting the chopper track according to the chopper track adjustment instruction set. The technical problem that in the prior art, the forming quality is unstable due to impedance deviation in the lead machining process is solved.

Description

Technical Field

[0001] This invention relates to a method, system, and medium for controlling the trajectory and size of a cutting tool based on lead impedance feedback, belonging to the field of microelectronic packaging technology. Background Technology

[0002] With the rapid development of 5G communications, data centers, high-speed computing, and artificial intelligence, the microelectronics packaging field faces new challenges and opportunities, placing higher demands on the integrity of high-speed signal transmission (SI). Among current mainstream packaging processes, wire bonding remains a widely adopted interconnection method due to its unique advantages, especially in system-in-package (SiP) and high-pin-count chips, where it plays a crucial role in ensuring the performance of microelectronic packaging.

[0003] Traditional wire bonding processes primarily rely on setting the wedge trajectory and selecting wedge heads of fixed specifications to effectively control the geometry of the lead wires, thereby meeting the requirements for package dimensions, solder joint locations, and process parameters. In actual production, gold or copper wires are typically used. A wedge is used to perform a series of spot welding and wire pulling operations between the chip pads and substrate pins, ultimately forming a three-dimensional lead structure with specific height, bending radius, and path. However, existing technologies suffer from unstable molding quality due to impedance deviations during lead wire processing, severely impacting the performance and reliability of microelectronic packaging. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for controlling the cutting trajectory and cutter head size based on lead wire impedance feedback. By dynamically controlling the cutting trajectory and cutter head size through impedance feedback, the invention solves the technical problem of unstable forming quality caused by impedance deviation during lead wire processing in the prior art.

[0005] To solve the above-mentioned technical problems, the present invention is implemented using the following technical solution:

[0006] In a first aspect, the present invention provides a method for controlling the trajectory and head size of a cutting tool based on lead impedance feedback, comprising:

[0007] Obtain the impedance parameters of the target lead, including the average impedance, impedance fluctuation range, and time-domain reflection characteristic curve;

[0008] The impedance deviation of the target lead is obtained by comparing the impedance parameters with the preset impedance parameter range.

[0009] If the impedance deviation exceeds a preset threshold, repeat the following steps until the impedance deviation is within the preset threshold range:

[0010] Based on the impedance deviation, a set of chopping trajectory adjustment instructions is output based on a pre-trained impedance-trajectory mapping model.

[0011] The compatibility of the tool head size is evaluated based on the impedance variation trend of the target lead over multiple machining cycles and the set of cutting trajectory adjustment instructions.

[0012] If the blade size is not suitable, switch to a blade with the appropriate size;

[0013] If the cutter head size is suitable, the target lead is optimized through a secondary forming process and the impedance parameters of the target lead are obtained.

[0014] The impedance deviation of the target lead is obtained by comparing the impedance parameter with the preset impedance parameter range, and it is determined whether the impedance deviation is within the preset threshold range.

[0015] If the impedance deviation is within the preset threshold range, the chopping trajectory is adjusted according to the chopping trajectory adjustment instruction set.

[0016] Furthermore, the impedance parameters of the target lead after bonding are obtained by performing characteristic impedance testing based on the four-terminal probe test method or the time-domain reflectometry method.

[0017] Furthermore, the impedance parameters of the target lead after bonding are obtained by performing characteristic impedance testing based on the four-terminal probe test method or the time-domain reflectometry method, including:

[0018] Based on the geometry and length of the target lead after bonding, determine the measurement start point and measurement end point and delineate the effective test area;

[0019] Based on the four-end probe test method or the time-domain reflectometry test method, the excitation end and the measurement end are arranged in the effective test area to input the test signal into the target lead after bonding;

[0020] By measuring the reflected signal waveform and propagation delay of the bonded target lead within a preset frequency range, the time-domain reflection characteristic curve of the bonded target lead is extracted.

[0021] Based on the time-domain characteristics of the bonded target lead, the equivalent characteristic impedance of the bonded target lead is calculated using the reflected wave packet analysis method or the impedance fitting method. The impedance values ​​of multiple test points are collected throughout the test cycle, and the average impedance and impedance fluctuation range are obtained through statistical analysis.

[0022] Further, the impedance deviation of the target lead is obtained by comparing the impedance parameters with a preset impedance parameter range, including:

[0023] Construct a set of impedance characteristic vectors for the target lead based on its impedance parameters;

[0024] A set of reference impedance feature vectors is generated based on a preset reference range of impedance parameters;

[0025] Based on the difference between the impedance characteristic vector set and the reference impedance characteristic vector set, the impedance deviation of the target lead is calculated using the vector distance determination method.

[0026] Based on the changing trend of the impedance deviation of the target lead within the preset frequency range, the directional characteristics and dominant influencing parameters of the impedance deviation are extracted as the impedance deviation of the target lead.

[0027] Furthermore, the network structure of the impedance-trajectory mapping model includes:

[0028] The input layer is used to receive the impedance deviation of the target lead and extract the direction and magnitude of the impedance deviation of the target lead, encode it into an impedance deviation vector, and input it into the first hidden layer.

[0029] A multi-node nonlinear transformation layer is used to extract features and expand the dimension of the impedance deviation vector, capture the cleaving trajectory corresponding to the impedance deviation vector under different preset deviation ranges, and generate cleaving trajectory features.

[0030] The compressed sensing layer is used to compress and map the chopping trajectory features and generate chopping trajectory adjustment command features corresponding to the chopping trajectory features.

[0031] The instruction generation preprocessing layer is used to reorganize and classify the characteristics of the chopping blade trajectory adjustment instruction. It processes the four independent sub-channels of chopping blade height, chopping blade tilt angle, chopping blade rotation amplitude, and chopping blade swing period respectively to generate input and output layers for chopping blade height trajectory adjustment instruction, chopping blade tilt angle trajectory adjustment instruction, chopping blade rotation amplitude trajectory adjustment instruction, and chopping blade swing period trajectory adjustment instruction.

[0032] The output layer is a multi-channel regression output layer used to output the chopping blade height trajectory adjustment command, the chopping blade tilt angle trajectory adjustment command, the chopping blade rotation amplitude trajectory adjustment command, and the chopping blade swing cycle trajectory adjustment command, thus obtaining a set of chopping blade trajectory adjustment commands.

[0033] Furthermore, the set of chopping blade trajectory adjustment commands includes chopping blade height trajectory adjustment commands, chopping blade tilt angle trajectory adjustment commands, chopping blade rotation amplitude trajectory adjustment commands, and chopping blade swing period trajectory adjustment commands, wherein the training method of the impedance-trajectory mapping model includes:

[0034] Collect historical bonding data of the lead wire, including historical impedance deviation of the lead wire and corresponding adjustment data of the cutting tool height trajectory, cutting tool tilt angle trajectory, cutting tool rotation amplitude trajectory, and cutting tool swing period trajectory.

[0035] Based on the corresponding chopping height trajectory adjustment data, chopping angle trajectory adjustment data, chopping rotation amplitude trajectory adjustment data, and chopping swing cycle trajectory adjustment data, generate chopping height trajectory adjustment commands, chopping angle trajectory adjustment commands, chopping rotation amplitude trajectory adjustment commands, and chopping swing cycle trajectory adjustment commands, and generate a set of historical chopping trajectory adjustment commands;

[0036] Based on the set of historical impedance deviation and historical cleaving trajectory adjustment instructions, a mapping database between the set of historical impedance deviation and historical cleaving trajectory adjustment instructions is constructed according to a preset data structure and storage method.

[0037] Based on the mapping database, a training set is constructed using historical impedance deviation as input labels and historical cleaving trajectory adjustment instruction set as output labels.

[0038] The impedance-trajectory mapping model is trained using a machine learning algorithm based on the training set. The parameters of the impedance-trajectory mapping model are adjusted by grid search to reduce the error between the true value and the predicted value of the blade trajectory adjustment command set, thus obtaining the trained impedance-trajectory mapping model.

[0039] Furthermore, the compatibility of the tool head size is evaluated based on the impedance variation trend of the target lead over multiple machining cycles and the set of wedge trajectory adjustment instructions, including:

[0040] Obtain the impedance parameters of the target lead over multiple processing cycles, and construct a lead impedance change trend vector based on the changing trend of the impedance parameters over multiple processing cycles.

[0041] Extract the rate of change, fluctuation amplitude, and abnormal jump characteristics of the lead impedance change trend vector;

[0042] Based on the set of chopping trajectory adjustment instructions, obtain the height, tilt angle, rotation amplitude, and swing period of the chopping blade after trajectory adjustment;

[0043] Based on the adjusted height, tilt angle, rotation amplitude, and oscillation period of the cleaving blade, a theoretical motion envelope model of the blade head is generated.

[0044] The rate of change, fluctuation amplitude, and abnormal jump characteristics of the lead impedance change trend vector are fitted and matched with the theoretical motion envelope model of the cutter head. Based on the synergy evaluation index, a synergy analysis is performed to determine whether the cutter head size is suitable.

[0045] If the synergy evaluation index does not meet the dynamic adaptation threshold, it is determined that the cutter head size is not compatible.

[0046] If the synergy evaluation index meets the dynamic adaptation threshold, it is determined to be a cutter head size adaptation.

[0047] Furthermore, the chopping trajectory is adjusted using a six-axis motion control platform based on the set of chopping trajectory adjustment instructions.

[0048] Secondly, the present invention provides a chopping tool trajectory and tool head size control system based on lead wire impedance feedback, for implementing the chopping tool trajectory and tool head size control method based on lead wire impedance feedback as described in the first aspect.

[0049] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for controlling the cutting trajectory and cutting head size based on lead impedance feedback as described in the first aspect.

[0050] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0051] This invention acquires the target lead impedance parameters in real time and accurately calculates the impedance deviation. It uses an impedance-trajectory mapping model to dynamically output commands for adjusting the trajectory of the cutting tool, including the cutting tool height trajectory, the cutting tool tilt angle trajectory, the cutting tool rotation amplitude trajectory, and the cutting tool swing period trajectory. At the same time, it evaluates and adapts the cutting tool size based on the impedance change trend. Finally, it optimizes the cutting tool trajectory based on the set of cutting tool trajectory adjustment commands that meet the threshold requirements. This effectively solves the problem of unstable lead wire forming quality caused by impedance deviation and significantly improves the integrity of high-speed signal transmission. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating a method for controlling the cutting trajectory and cutting head size based on lead impedance feedback, provided in an embodiment of the present invention. Detailed Implementation

[0053] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0054] Example 1

[0055] like Figure 1 As shown, this embodiment introduces a method for controlling the cutting trajectory and cutting head size based on lead impedance feedback, including:

[0056] Step 1: Obtain the impedance parameters of the target lead, including the average impedance, impedance fluctuation range, and time-domain reflection characteristic curve.

[0057] This invention achieves a fully parameterized characterization of the impedance characteristics of the target lead by obtaining its impedance parameters, providing a precise measurement data basis for subsequent impedance deviation analysis and ensuring the comprehensiveness and objectivity of the impedance assessment.

[0058] Step 2: Obtain the impedance deviation of the target lead by comparing the impedance parameters with the preset impedance parameter range.

[0059] Based on the comparative analysis of measured impedance parameters and preset impedance parameter ranges, this invention can quantify the impedance deviation of the target lead, clarify the direction and magnitude of the impedance anomaly, provide a clear target guide for the adjustment of the cutting trajectory, and avoid blind adjustment.

[0060] Step 3: If the impedance deviation exceeds a preset threshold, repeat the following steps until the impedance deviation is within the preset threshold range:

[0061] Step 3.1: Based on the impedance deviation, make a prediction based on the pre-trained impedance-trajectory mapping model and output a set of chopping trajectory adjustment instructions.

[0062] This invention utilizes a pre-trained impedance-trajectory mapping model, combined with impedance deviation for prediction, to output a set of chopping trajectory adjustment instructions, including chopping height trajectory adjustment instructions, chopping angle trajectory adjustment instructions, chopping rotation amplitude trajectory adjustment instructions, and chopping swing cycle trajectory adjustment instructions. This achieves intelligent generation of adjustment strategies and improves the accuracy and efficiency of trajectory adjustment.

[0063] Step 3.2: Evaluate whether the tool head size is suitable based on the impedance change trend of the target lead over multiple machining cycles and the set of wedge trajectory adjustment instructions.

[0064] If the blade size is not suitable, switch to a blade with the appropriate size;

[0065] If the cutter head size is suitable, the target lead is optimized through a secondary forming process to obtain the impedance parameters of the target lead.

[0066] This invention dynamically evaluates the compatibility between the cutter head size and the target lead impedance variation trend. When the size is not compatible, it switches to a compatible cutter head, avoiding impedance optimization failure caused by poor physical contact. When the size is compatible, it optimizes the lead structure through a secondary forming process, improving impedance characteristics from a physical perspective and enhancing the reliability of impedance adjustment.

[0067] Step 3.3: Compare the impedance parameters with the preset impedance parameter range to obtain the impedance deviation of the target lead, and determine whether the impedance deviation is within the preset threshold range.

[0068] This invention verifies whether the impedance deviation is controlled within the threshold by comparing the adjusted impedance parameters with the preset range again in a closed loop. This forms a closed-loop control mechanism from adjustment to verification, and then from verification to feedback, ensuring the effectiveness of the adjustment process and avoiding the problems of under-adjustment or over-adjustment.

[0069] Step 4: If the impedance deviation is within the preset threshold range, adjust the chopping trajectory according to the chopping trajectory adjustment instruction set.

[0070] This invention, based on the closed-loop verification of the cutting tool trajectory adjustment command, achieves precise control of the cutting tool's movement trajectory, ensuring the stability of the target lead impedance from the process execution level, and ultimately improving the quality and consistency of wire bonding.

[0071] Example 2

[0072] This embodiment describes the implementation steps of a method for controlling the cutting tool trajectory and cutting head size based on lead impedance feedback, including:

[0073] Step 1: Obtain the impedance parameters of the target lead, including the average impedance, impedance fluctuation range, and time-domain reflection characteristic curve.

[0074] This embodiment uses a four-terminal probe test or a time-domain reflectometry method to perform characteristic impedance testing on the bonded target lead to obtain its impedance parameters, including:

[0075] Step 1.1: Based on the geometry and length of the target lead after bonding, determine the measurement start point and measurement end point, and delineate the effective test area.

[0076] Step 1.2: According to the four-end probe test method or time domain reflectance test method, arrange the excitation end and the measurement end in the effective test area and input the test signal into the target lead after bonding.

[0077] Step 1.3: Extract the time-domain reflection characteristic curve of the bonded target lead by measuring the reflected signal waveform and propagation delay within a preset frequency range.

[0078] Step 1.4: Based on the time-domain characteristics of the bonded target lead, calculate the equivalent characteristic impedance of the bonded target lead using the reflected wave packet analysis method or impedance fitting method, and collect impedance values ​​at multiple test points throughout the entire test cycle. Statistical analysis is then used to obtain the average impedance and impedance fluctuation range.

[0079] Step 2: Obtain the impedance deviation of the target lead by comparing the impedance parameters with the preset impedance parameter range.

[0080] Step 2.1: Construct the impedance characteristic vector set of the target lead based on the impedance parameters of the target lead.

[0081] Step 2.2: Generate a set of reference impedance characteristic vectors based on the preset impedance parameter reference range.

[0082] Step 2.3: Based on the difference between the impedance characteristic vector set and the reference impedance characteristic vector set, the impedance deviation of the target lead is calculated using the vector distance determination method.

[0083] Step 2.4: Based on the changing trend of the impedance deviation of the target lead within the preset frequency range, extract the directional characteristics and dominant influencing parameters of the impedance deviation as the impedance deviation of the target lead.

[0084] Step 3: If the impedance deviation exceeds a preset threshold, repeat the following steps until the impedance deviation is within the preset threshold range:

[0085] Step 3.1: Based on the impedance deviation, make a prediction based on the pre-trained impedance-trajectory mapping model and output a set of chopping trajectory adjustment instructions.

[0086] In this embodiment, the set of chopping blade trajectory adjustment commands includes chopping blade height trajectory adjustment commands, chopping blade tilt angle trajectory adjustment commands, chopping blade rotation amplitude trajectory adjustment commands, and chopping blade swing cycle trajectory adjustment commands.

[0087] In this embodiment, the network structure of the impedance-trajectory mapping model includes:

[0088] The input layer is used to receive the impedance deviation of the target lead and extract the direction and magnitude of the impedance deviation of the target lead, encode it into an impedance deviation vector, and input it into the first hidden layer.

[0089] A multi-node nonlinear transformation layer is used to extract features and expand the dimension of the impedance deviation vector, capture the cleaving trajectory corresponding to the impedance deviation vector under different preset deviation ranges, and generate cleaving trajectory features.

[0090] The compressed sensing layer is used to compress and map the chopping trajectory features and generate chopping trajectory adjustment command features corresponding to the chopping trajectory features.

[0091] The instruction generation preprocessing layer is used to reorganize and classify the characteristics of the chopping blade trajectory adjustment instruction. It processes the four independent sub-channels of chopping blade height, chopping blade tilt angle, chopping blade rotation amplitude, and chopping blade swing period respectively to generate input and output layers for chopping blade height trajectory adjustment instruction, chopping blade tilt angle trajectory adjustment instruction, chopping blade rotation amplitude trajectory adjustment instruction, and chopping blade swing period trajectory adjustment instruction.

[0092] The output layer is a multi-channel regression output layer used to output the chopping blade height trajectory adjustment command, the chopping blade tilt angle trajectory adjustment command, the chopping blade rotation amplitude trajectory adjustment command, and the chopping blade swing cycle trajectory adjustment command, thus obtaining a set of chopping blade trajectory adjustment commands.

[0093] In this embodiment, the set of chopping blade trajectory adjustment commands includes chopping blade height trajectory adjustment commands, chopping blade tilt angle trajectory adjustment commands, chopping blade rotation amplitude trajectory adjustment commands, and chopping blade swing period trajectory adjustment commands. The training method for the impedance-trajectory mapping model includes:

[0094] Collect historical bonding data of the lead wire, including historical impedance deviation of the lead wire and corresponding adjustment data of the cutting tool height trajectory, cutting tool tilt angle trajectory, cutting tool rotation amplitude trajectory, and cutting tool swing period trajectory.

[0095] Based on the corresponding chopping height trajectory adjustment data, chopping angle trajectory adjustment data, chopping rotation amplitude trajectory adjustment data, and chopping swing cycle trajectory adjustment data, generate chopping height trajectory adjustment commands, chopping angle trajectory adjustment commands, chopping rotation amplitude trajectory adjustment commands, and chopping swing cycle trajectory adjustment commands, and generate a set of historical chopping trajectory adjustment commands;

[0096] Based on the set of historical impedance deviation and historical cleaving trajectory adjustment instructions, a mapping database between the set of historical impedance deviation and historical cleaving trajectory adjustment instructions is constructed according to a preset data structure and storage method.

[0097] Based on the mapping database, a training set is constructed using historical impedance deviation as input labels and historical cleaving trajectory adjustment instruction set as output labels.

[0098] The impedance-trajectory mapping model is trained using a machine learning algorithm based on the training set. The parameters of the impedance-trajectory mapping model are adjusted by grid search to reduce the error between the true value and the predicted value of the blade trajectory adjustment command set, thus obtaining the trained impedance-trajectory mapping model.

[0099] Step 3.2: Evaluate whether the tool head size is suitable based on the impedance change trend of the target lead over multiple machining cycles and the set of wedge trajectory adjustment instructions.

[0100] If the blade size is not suitable, switch to a blade with the appropriate size;

[0101] If the cutter head size is suitable, the target lead is optimized through a secondary forming process to obtain the impedance parameters of the target lead.

[0102] In this implementation, the compatibility of the tool head size is evaluated based on the impedance change trend of the target lead over multiple machining cycles and the set of wedge trajectory adjustment instructions, including:

[0103] Obtain the impedance parameters of the target lead over multiple processing cycles, and construct a lead impedance change trend vector based on the changing trend of the impedance parameters over multiple processing cycles.

[0104] Extract the rate of change, fluctuation amplitude, and abnormal jump characteristics of the lead impedance change trend vector;

[0105] Based on the set of chopping trajectory adjustment instructions, obtain the height, tilt angle, rotation amplitude, and swing period of the chopping blade after trajectory adjustment;

[0106] Based on the adjusted height, tilt angle, rotation amplitude, and oscillation period of the cleaving blade, a theoretical motion envelope model of the blade head is generated.

[0107] The rate of change, fluctuation amplitude, and abnormal jump characteristics of the lead impedance change trend vector are fitted and matched with the theoretical motion envelope model of the cutter head. Based on the synergy evaluation index, a synergy analysis is performed to determine whether the cutter head size is suitable.

[0108] If the synergy evaluation index does not meet the dynamic adaptation threshold, it is determined that the cutter head size is not compatible.

[0109] If the synergy evaluation index meets the dynamic adaptation threshold, it is determined to be a cutter head size adaptation.

[0110] Step 3.3: Compare the impedance parameters with the preset impedance parameter range to obtain the impedance deviation of the target lead, and determine whether the impedance deviation is within the preset threshold range.

[0111] Step 4: If the impedance deviation is within the preset threshold range, adjust the chopping trajectory according to the chopping trajectory adjustment instruction set.

[0112] In this embodiment, a six-axis motion control platform is used to adjust the chopping trajectory according to the set of chopping trajectory adjustment instructions.

[0113] Example 3

[0114] Based on the same inventive concept as Embodiment 1, this embodiment introduces a chopping tool trajectory and cutter head size control system based on lead wire impedance feedback, which is used to implement the chopping tool trajectory and cutter head size control method based on lead wire impedance feedback as described in Embodiment 1 or 2.

[0115] For specific functional implementation details, please refer to the relevant content in Embodiment 1 or 2, which will not be repeated here.

[0116] Example 4

[0117] Based on the same inventive concept as other embodiments, this embodiment describes a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of the methods of Embodiment 1 or 2 described above.

[0118] In summary, this invention acquires the target lead impedance parameters in real time and accurately calculates the impedance deviation. It then uses an impedance-trajectory mapping model to dynamically output commands for adjusting the chopping height trajectory, chopping angle trajectory, chopping rotation amplitude trajectory, and chopping oscillation period trajectory. Simultaneously, it evaluates and adapts the chopping head size based on impedance change trends. Finally, it optimizes the chopping trajectory based on adjustment commands that meet threshold requirements, effectively solving the problem of unstable lead forming quality caused by impedance deviation and significantly improving the integrity of high-speed signal transmission.

[0119] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0121] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0122] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0123] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for controlling the trajectory and head size of a cutting tool based on lead impedance feedback, characterized in that, include: Obtain the impedance parameters of the target lead, including the average impedance, impedance fluctuation range, and time-domain reflection characteristic curve; The impedance deviation of the target lead is obtained by comparing the impedance parameters with the preset impedance parameter range. If the impedance deviation exceeds a preset threshold, repeat the following steps until the impedance deviation is within the preset threshold range: Based on the impedance deviation, a set of chopping trajectory adjustment instructions is output based on a pre-trained impedance-trajectory mapping model. The compatibility of the tool head size is evaluated based on the impedance variation trend of the target lead over multiple machining cycles and the set of cutting trajectory adjustment instructions. If the blade size is not suitable, switch to a blade with the appropriate size; If the cutter head size is suitable, the target lead is optimized through a secondary forming process and the impedance parameters of the target lead are obtained. The impedance deviation of the target lead is obtained by comparing the impedance parameter with the preset impedance parameter range, and it is determined whether the impedance deviation is within the preset threshold range. If the impedance deviation is within the preset threshold range, the chopping trajectory is adjusted according to the chopping trajectory adjustment instruction set.

2. The method for controlling the cutting trajectory and cutting head size based on lead impedance feedback according to claim 1, characterized in that, The impedance parameters of the target lead after bonding are obtained by characteristic impedance testing using the four-terminal probe test method or the time-domain reflectometry method.

3. The method for controlling the cutting trajectory and cutting head size based on lead impedance feedback according to claim 2, characterized in that, The impedance parameters of the bonded target lead are obtained by performing characteristic impedance testing using the four-terminal probe test method or the time-domain reflectometry method, including: Based on the geometry and length of the target lead after bonding, determine the measurement start point and measurement end point and delineate the effective test area; Based on the four-end probe test method or the time-domain reflectometry test method, the excitation end and the measurement end are arranged in the effective test area to input the test signal into the target lead after bonding; By measuring the reflected signal waveform and propagation delay of the bonded target lead within a preset frequency range, the time-domain reflection characteristic curve of the bonded target lead is extracted. Based on the time-domain characteristics of the bonded target lead, the equivalent characteristic impedance of the bonded target lead is calculated using the reflected wave packet analysis method or the impedance fitting method. The impedance values ​​of multiple test points are collected throughout the test cycle, and the average impedance and impedance fluctuation range are obtained through statistical analysis.

4. The method for controlling the cutting trajectory and cutting head size based on lead impedance feedback according to claim 1, characterized in that, The impedance deviation of the target lead is obtained by comparing the impedance parameters with a preset impedance parameter range, including: Construct a set of impedance characteristic vectors for the target lead based on its impedance parameters; A set of reference impedance feature vectors is generated based on a preset reference range of impedance parameters; Based on the difference between the impedance characteristic vector set and the reference impedance characteristic vector set, the impedance deviation of the target lead is calculated using the vector distance determination method. Based on the changing trend of the impedance deviation of the target lead within the preset frequency range, the directional and amplitude characteristic data of the impedance deviation are extracted and quantified as the impedance deviation of the target lead.

5. The method for controlling the cutting trajectory and cutting head size based on lead impedance feedback according to claim 1, characterized in that, The set of chopping blade trajectory adjustment commands includes chopping blade height trajectory adjustment commands, chopping blade tilt angle trajectory adjustment commands, chopping blade rotation amplitude trajectory adjustment commands, and chopping blade swing period trajectory adjustment commands. The network structure of the impedance-trajectory mapping model includes: The input layer is used to receive the impedance deviation of the target lead and extract the direction and magnitude of the impedance deviation of the target lead, encode it into an impedance deviation vector, and input it into the first hidden layer. A multi-node nonlinear transformation layer is used to extract features and expand the dimension of the impedance deviation vector, capture the cleaving trajectory corresponding to the impedance deviation vector under different preset deviation ranges, and generate cleaving trajectory features. The compressed sensing layer is used to compress and map the chopping trajectory features and generate chopping trajectory adjustment command features corresponding to the chopping trajectory features. The instruction generation preprocessing layer is used to reorganize and classify the characteristics of the chopping blade trajectory adjustment instruction. It processes the four independent sub-channels of chopping blade height, chopping blade tilt angle, chopping blade rotation amplitude, and chopping blade swing period respectively to generate input and output layers for chopping blade height trajectory adjustment instruction, chopping blade tilt angle trajectory adjustment instruction, chopping blade rotation amplitude trajectory adjustment instruction, and chopping blade swing period trajectory adjustment instruction. The output layer is a multi-channel regression output layer used to output the chopping blade height trajectory adjustment command, the chopping blade tilt angle trajectory adjustment command, the chopping blade rotation amplitude trajectory adjustment command, and the chopping blade swing cycle trajectory adjustment command, thus obtaining a set of chopping blade trajectory adjustment commands.

6. The method for controlling the cutting trajectory and cutting head size based on lead impedance feedback according to claim 5, characterized in that, The training method for the impedance-trajectory mapping model includes: Collect historical bonding data of the lead wire, including historical impedance deviation of the lead wire and corresponding adjustment data of the cutting tool height trajectory, cutting tool tilt angle trajectory, cutting tool rotation amplitude trajectory, and cutting tool swing period trajectory. Based on the corresponding chopping height trajectory adjustment data, chopping angle trajectory adjustment data, chopping rotation amplitude trajectory adjustment data, and chopping swing cycle trajectory adjustment data, generate chopping height trajectory adjustment commands, chopping angle trajectory adjustment commands, chopping rotation amplitude trajectory adjustment commands, and chopping swing cycle trajectory adjustment commands, and generate a set of historical chopping trajectory adjustment commands; Based on the set of historical impedance deviation and historical cleaving trajectory adjustment instructions, a mapping database between the set of historical impedance deviation and historical cleaving trajectory adjustment instructions is constructed according to a preset data structure and storage method. Based on the mapping database, a training set is constructed using historical impedance deviation as input labels and historical cleaving trajectory adjustment instruction set as output labels. The impedance-trajectory mapping model is trained using a machine learning algorithm based on the training set. The parameters of the impedance-trajectory mapping model are adjusted by grid search to reduce the error between the true value and the predicted value of the blade trajectory adjustment command set, thus obtaining the trained impedance-trajectory mapping model.

7. The method for controlling the cutting trajectory and cutting head size based on lead impedance feedback according to claim 1, characterized in that, The tool head size is evaluated based on the impedance variation trend of the target lead over multiple machining cycles and the set of wedge trajectory adjustment instructions, including: Obtain the impedance parameters of the target lead over multiple processing cycles, and construct a lead impedance change trend vector based on the changing trend of the impedance parameters over multiple processing cycles. Extract the rate of change, fluctuation amplitude, and abnormal jump characteristics of the lead impedance change trend vector; Based on the set of chopping trajectory adjustment instructions, obtain the height, tilt angle, rotation amplitude, and swing period of the chopping blade after trajectory adjustment; Based on the adjusted height, tilt angle, rotation amplitude, and oscillation period of the cleaving blade, a theoretical motion envelope model of the blade head is generated. The rate of change, fluctuation amplitude, and abnormal jump characteristics of the lead impedance change trend vector are fitted and matched with the theoretical motion envelope model of the cutter head. Based on the synergy evaluation index, a synergy analysis is performed to determine whether the cutter head size is suitable. If the synergy evaluation index does not meet the dynamic adaptation threshold, it is determined that the cutter head size is not compatible. If the synergy evaluation index meets the dynamic adaptation threshold, it is determined to be a cutter head size adaptation.

8. The method for controlling the cutting trajectory and cutting head size based on lead impedance feedback according to claim 1, characterized in that, The chopping trajectory is adjusted using a six-axis motion control platform based on the set of chopping trajectory adjustment instructions.

9. A system for adjusting the trajectory and head size of a cutting tool based on lead impedance feedback, characterized in that, This method is used to implement the method for controlling the cutting trajectory and cutting head size based on lead impedance feedback as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for controlling the cutting trajectory and cutting head size based on lead impedance feedback as described in any one of claims 1-8.

Images