Temperature monitoring method and semiconductor process apparatus

Abstract

The application discloses a temperature monitoring method and a semiconductor process equipment. The method comprises the following steps: obtaining a plurality of measured temperature values of monitoring points of the semiconductor process equipment; calculating a weighted standard deviation with a constraint condition according to the plurality of measured temperature values, wherein the constraint condition comprises that a weighted coefficient is equal to or greater than 1; if the weighted standard deviation with the constraint condition is greater than a temperature deviation threshold, determining that the temperature is abnormal; otherwise, determining that the temperature is normal. When the weighted standard deviation with the constraint condition is calculated, because the constraint condition comprises that the weighted coefficient is equal to or greater than 1, the weighted standard deviation obtained can realize the effect of standard deviation amplification compared with the ordinary standard deviation, and then by comparison with the temperature deviation threshold, the temperature short-time rapid falling condition can be monitored and captured more timely and accurately, so that subsequent measures can be taken, and the normal process effect is prevented from being affected or the process failure is prevented.

Description

Technical Field

[0001] This application belongs to the field of semiconductor technology, and in particular relates to a temperature monitoring method and semiconductor process equipment. Background Technology

[0002] Vertical furnaces are crucial equipment in semiconductor manufacturing processes, used to create semiconductor devices. They control the contact between silicon wafers and doping gases at high temperatures to perform doping processes, thereby obtaining the desired N-type or P-type semiconductor materials. With the advancement of integrated circuit manufacturing processes, vertical furnaces are continuously being upgraded to achieve more precise temperature control, which is one of the key performance indicators for vertical furnace equipment.

[0003] Vertical furnace equipment typically uses thermocouples to measure temperature. After prolonged use, thermocouples may experience issues such as loose connections or intermittent detachment due to vibration, leading to intermittent changes in the thermocouple output and a rapid drop in the measured temperature. This can cause short-term temperature instability, affecting normal process performance and, in severe cases, even causing process failure.

[0004] The commonly used temperature monitoring method compares the measured temperature value with the set temperature value. If the temperature deviation is within the performance specification range, it is considered reasonable; if the temperature deviation exceeds the performance specification range, it is considered that the temperature control has deviated. This method cannot detect situations where the temperature drops rapidly in a short period of time. Therefore, the industry urgently needs a solution that can monitor and capture such rapid temperature drops in a timely and accurate manner. Summary of the Invention

[0005] The purpose of this application is to provide a temperature monitoring method and semiconductor process equipment to solve the problem in related technologies that it is impossible to monitor and capture short-term rapid temperature drops in a timely and accurate manner.

[0006] To achieve the above objectives, the embodiments of this application adopt the following technical solutions:

[0007] In a first aspect, embodiments of this application provide a temperature monitoring method, comprising: acquiring multiple measured temperature values ​​at monitoring points of a semiconductor process equipment; calculating a weighted standard deviation with constraints based on the multiple measured temperature values, wherein the constraints include a weighting coefficient equal to or greater than 1; determining that the temperature is abnormal if the weighted standard deviation with constraints is greater than a temperature deviation threshold; and determining that the temperature is normal if the weighted standard deviation with constraints is equal to or less than the temperature deviation threshold.

[0008] In a second aspect, embodiments of this application provide a semiconductor process apparatus, including: a temperature controller, a process tube, and a thermocouple disposed inside the process tube, wherein the temperature controller is used to implement the steps of the temperature monitoring method as described in the first aspect of this application.

[0009] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0010] In this embodiment of the application, when monitoring the temperature of semiconductor process equipment, a weighted standard deviation with constraints is calculated based on multiple measured temperature values ​​at monitoring points of the semiconductor process equipment. The constraints include a weighting coefficient equal to or greater than 1. If the weighted standard deviation with constraints is greater than a temperature deviation threshold, an abnormal temperature is determined; if the weighted standard deviation with constraints is equal to or less than the temperature deviation threshold, the temperature is determined to be normal. In calculating the weighted standard deviation with constraints, since the constraints include a weighting coefficient equal to or greater than 1, the calculated weighted standard deviation is equal to or greater than the ordinary standard deviation. Because the ordinary standard deviation, compared to the temperature deviation between the measured temperature value and the set temperature value in related technologies, can amplify the temperature deviation between multiple measured temperature values, a weighted standard deviation equal to or greater than the ordinary standard deviation can further amplify the temperature deviation between multiple measured temperature values. Then, by comparing it with the temperature deviation threshold, it can more timely and accurately monitor and capture situations where the temperature drops rapidly in a short period, facilitating subsequent measures to avoid affecting normal process performance or preventing process failure. Attached Figure Description

[0011] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0012] Figure 1 A schematic diagram of a vertical furnace device provided for one embodiment of this application;

[0013] Figure 2 A schematic diagram illustrating a rapid temperature drop over a short period of time, provided for one embodiment of this application;

[0014] Figure 3 This is a schematic diagram of a commonly used temperature monitoring scheme;

[0015] Figure 4 A schematic flowchart of a temperature monitoring method provided for one embodiment of this application;

[0016] Figure 5 A schematic diagram illustrating the determination of whether a temperature is abnormal based on a deviation judgment threshold, provided for one embodiment of this application;

[0017] Figure 6 A schematic flowchart of a temperature monitoring method provided for another embodiment of this application;

[0018] Figure 7 This is a schematic diagram illustrating how the calculation result of array A is stored in array B, as provided in one embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, "and / or" in this application indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship. It should be noted that all data involved in this application was obtained with the user's authorization.

[0021] like Figure 1 As shown, the vertical furnace equipment has a process tube 2 inside the furnace body 1, a boat 3 inside the process tube 2, and a batch of silicon wafers placed on the boat 3. The exhaust pipe 4 and the exhaust damper 5 are used to remove waste process gases from inside the furnace body 1. The heating unit 6 (e.g., heating wire) is used to heat the furnace body 1, and the thermocouple 7 is used to monitor the real-time temperature of each temperature zone inside the furnace body 1 and participates in the temperature control of the temperature controller.

[0022] like Figure 2 As shown, vertical furnace equipment typically uses thermocouples to measure temperature. After prolonged use, thermocouples may experience issues such as loose connections or intermittent detachment due to vibration, leading to intermittent changes in the thermocouple output and a rapid drop in the measured temperature. This can cause short-term temperature control instability, affecting normal process performance and, in severe cases, even process failure.

[0023] like Figure 3 As shown, a common temperature monitoring method compares the measured temperature value with the set temperature value (Set point). If the temperature deviation is within the performance specification range (i.e., ...), the temperature is monitored. Figure 3If the temperature deviation is within the upper and lower bounds of the performance specifications, the temperature deviation is considered reasonable; if the temperature deviation exceeds the performance specification range, the temperature control is considered to be in error. This approach cannot detect situations where the temperature drops rapidly in a short period of time. Therefore, this application proposes a temperature monitoring method, semiconductor process equipment, and storage medium to monitor and capture such rapid temperature drops in a timely and accurate manner.

[0024] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0025] Figure 4 This is a schematic flowchart illustrating a temperature monitoring method according to one embodiment of this application. Figure 4 As shown, the temperature monitoring method of this application embodiment may specifically include the following steps:

[0026] S401 acquires multiple measured temperature values ​​from monitoring points of semiconductor process equipment.

[0027] In this embodiment of the application, the subject of the temperature monitoring method is a temperature monitoring device, which can be installed in semiconductor process equipment, such as vertical furnace equipment used to produce semiconductor devices such as silicon wafers.

[0028] The semiconductor process equipment is equipped with multiple temperature measurement points, each of which can serve as a monitoring point for implementing the temperature monitoring method of the embodiments of this application.

[0029] When the process is in the main process stage of the normal process recipe, the temperature value of the monitoring point measured by the thermocouple is obtained in real time within the preset sampling time and recorded as the measured temperature value.

[0030] As a first feasible implementation method, the measured temperature value of the semiconductor process equipment can be obtained once at set time intervals.

[0031] As a second feasible implementation method, the measured temperature values ​​of the semiconductor process equipment can be obtained at multiple preset time points.

[0032] Multiple measured temperature values ​​acquired within a sampling period can be stored in a fixed-length array A. Each time array A is full, a subsequent step is executed to calculate the weighted standard deviation with constraints. Then, array A is cleared to store the second batch of measured temperature values, and the weighted standard deviation calculation with constraints continues. By reusing the storage space of array A, memory is saved, which is compatible with the hardware requirements of the machine controller.

[0033] S402, calculate the weighted standard deviation with constraints based on multiple measured temperature values. The constraints include a weighting coefficient equal to or greater than 1.

[0034] In this embodiment, based on the multiple measured temperature values ​​obtained in step S401, a weighted standard deviation S is calculated for these multiple measured temperature values. The calculation of the weighted standard deviation S is subject to constraints, including that the weighting coefficient used in calculating the weighted standard deviation S is equal to or greater than 1. A weighting coefficient β equal to or greater than 1 allows the calculated weighted standard deviation S to be larger than the ordinary standard deviation, i.e., the weighted standard deviation S is equal to or greater than the ordinary standard deviation, achieving a standard deviation amplification effect. Since the ordinary standard deviation, compared to the temperature deviation between the measured temperature value and the set temperature value in related technologies, can already amplify the temperature deviation between multiple measured temperature values, a weighted standard deviation equal to or greater than the ordinary standard deviation can more effectively amplify the temperature deviation between multiple measured temperature values.

[0035] S403, determine whether the weighted standard deviation with constraints is greater than the temperature deviation threshold.

[0036] S404. If the weighted standard deviation with constraints is greater than the temperature deviation threshold, then a temperature anomaly is determined.

[0037] S405, if the weighted standard deviation with constraints is equal to or less than the temperature deviation threshold, then the temperature is determined to be normal.

[0038] In this embodiment, a temperature deviation threshold can be preset. It is then determined whether the weighted standard deviation S with constraints calculated in step S402 is greater than the temperature deviation threshold. If the weighted standard deviation S with constraints calculated in step S402 is greater than the temperature deviation threshold, the temperature is determined to be abnormal. Conversely, if the weighted standard deviation S with constraints calculated in step S402 is equal to or less than the temperature deviation threshold, the temperature is determined to be normal.

[0039] like Figure 5 As shown, the vertical axis represents the weighted standard deviation S with constraints. This is achieved by comparing the weighted standard deviation S with the temperature deviation threshold (i.e.,...). Figure 5 The deviation judgment threshold can be used to monitor and capture situations where the temperature drops rapidly in a short period of time in a timely and accurate manner.

[0040] In summary, the temperature monitoring method of this application, when monitoring the temperature of semiconductor process equipment, calculates a weighted standard deviation with constraints based on multiple measured temperature values ​​at monitoring points of the semiconductor process equipment. The constraints include a weighting coefficient equal to or greater than 1. If the weighted standard deviation with constraints is greater than a temperature deviation threshold, the temperature is determined to be abnormal; if the weighted standard deviation with constraints is equal to or less than the temperature deviation threshold, the temperature is determined to be normal. In calculating the weighted standard deviation with constraints, since the constraints include a weighting coefficient equal to or greater than 1, the calculated weighted standard deviation is equal to or greater than the ordinary standard deviation. Because the ordinary standard deviation, compared to the temperature deviation between the measured temperature value and the set temperature value in related technologies, can amplify the temperature deviation of multiple measured temperature values, the weighted standard deviation, which is equal to or greater than the ordinary standard deviation, can further amplify the temperature deviation between multiple measured temperature values. Then, by comparing it with the temperature deviation threshold, it can more timely and accurately monitor and capture situations where the temperature drops rapidly in a short period, facilitating subsequent measures to avoid affecting normal process performance or preventing process failure.

[0041] Figure 6 This is a schematic flowchart illustrating a temperature monitoring method according to another embodiment of this application. Figure 6 As shown, in Figure 4 Based on the illustrated embodiments, the temperature monitoring method of this application may specifically include the following steps:

[0042] Step S401 in the above embodiments may specifically include the following steps S601 and S602.

[0043] S601 determines whether the temperature field of semiconductor process equipment is stable.

[0044] In this embodiment, before initiating the short-term rapid temperature drop monitoring algorithm using weighted standard deviation, it can be determined whether the temperature field of the semiconductor process equipment is stable. If the temperature field is unstable, a temperature control anomaly is determined, and there is no need to initiate the short-term rapid temperature drop monitoring algorithm using weighted standard deviation. If the temperature field is stable, the short-term rapid temperature drop monitoring algorithm using weighted standard deviation is initiated to further monitor whether a short-term rapid temperature drop occurs.

[0045] Those skilled in the art will understand that the algorithm for monitoring short-term rapid temperature drops using weighted standard deviation should only be activated when the temperature field is stable, so as to ensure monitoring effectiveness and avoid wasting computational resources.

[0046] Determining whether the temperature field of semiconductor process equipment is stable may include the following steps: calculating the temperature deviation between the currently measured temperature value and the set temperature value at the corresponding time; if the temperature deviation is less than a preset deviation threshold (corresponding to...). Figure 3 If the temperature deviation is within the upper and lower bounds of the preset deviation threshold, the temperature field of the semiconductor process equipment is determined to be stable; if the temperature deviation is equal to or greater than the preset deviation threshold, the temperature field of the semiconductor process equipment is determined to be unstable.

[0047] S602, if stable, acquires multiple measured temperature values ​​from monitoring points of the semiconductor process equipment.

[0048] In this embodiment, if the temperature field is stable, the algorithm for monitoring rapid temperature drops over short periods using weighted standard deviation is activated, i.e., the process of acquiring multiple measured temperature values ​​at monitoring points of the semiconductor process equipment continues. This step S602 is essentially the same as step S401 in the above embodiment, and will not be described again here.

[0049] S603 calculates the weighted standard deviation with constraints based on multiple measured temperature values. The constraints include a weighting coefficient equal to or greater than 1.

[0050] In this embodiment of the application, step S603 is the same as step S402 in the above embodiment, and will not be described again here.

[0051] As a feasible implementation method, the weighted standard deviation S with constraints can be calculated using the following formula:

[0052]

[0053] The constraints are:

[0054]

[0055] Where S is the weighted standard deviation with constraints, β is the weighting coefficient, and X... i Let be the measured temperature value at the i-th sampling time, and n be the number of samples within the sampling time. The average of n measured temperature values ​​within the sampling time period is α, where α is a preset real number greater than 1 and j is a natural number less than n.

[0056] S604 determines whether the weighted standard deviation with constraints is greater than the temperature deviation threshold.

[0057] S605, if the weighted standard deviation with constraints is greater than the temperature deviation threshold, then a temperature anomaly is determined.

[0058] S606, if the weighted standard deviation with constraints is equal to or less than the temperature deviation threshold, then the temperature is determined to be normal.

[0059] In this embodiment, steps S604-S606 are the same as steps S403-S405 in the above embodiments, and will not be repeated here.

[0060] For example:

[0061] Assuming the continuous sampled temperatures (measured temperatures) at the monitoring point are 400, 399.8, 399.6, 399.4, and 399.2, the result calculated using the general standard deviation formula is 0.316. The result calculated using the weighted standard deviation formula with constraints is 0.354. In the above formula, the value of j is chosen as 3, and α is taken as 1.5. If the temperature deviation threshold is 0.33, the general standard deviation method will not identify a temperature anomaly, while the weighted standard deviation method with constraints will. Since the measured temperature values ​​are continuously decreasing for three consecutive times, the weighting coefficient is set to 1.5. The amplification effect of the weighting coefficient takes effect, making the final calculated weighted standard deviation larger. If the same temperature deviation threshold is used for subsequent judgments, the weighted standard deviation can exceed the temperature deviation threshold more quickly, detecting the temperature anomaly.

[0062] It should be noted here that the temperature deviation threshold can be set to the default deviation threshold. The default deviation threshold is the sum of the offline standard deviation calculated based on the offline temperature value and the preset reserved value, that is, the default deviation threshold = offline standard deviation + reserved value.

[0063] Specifically, the standard deviation can be calculated based on the offline temperature values ​​of the main process stage for a typical process formulation, and denoted as the offline standard deviation. A reserve value is given based on prior experience, and a default deviation threshold is set as: offline standard deviation + reserve value. When the default deviation threshold is subsequently valid, if the weighted standard deviation calculated online is greater than the default deviation threshold, the temperature is determined to be abnormal. If the weighted standard deviation calculated online is less than or equal to the default deviation threshold, the temperature is determined to be normal. The default deviation threshold needs to be updated periodically. When the default deviation threshold is replaced by an online calculated threshold, the default deviation threshold becomes invalid.

[0064] Due to long-term use of the equipment, components may age or wear, rendering the original default deviation threshold inapplicable. Therefore, the default deviation threshold can be replaced with an online calculated threshold. Specifically, in the initial stage of equipment use, the initial value of the temperature deviation threshold can be set to the default deviation threshold. The weighted standard deviation with constraints calculated for the first time in this process recipe should be compared with this default deviation threshold, and subsequent weighted standard deviations with constraints should be compared with the new online calculated threshold. After the equipment has been used for a period of time, the initial value of the temperature deviation threshold can be set to the online calculated threshold obtained from the first calculation of the process recipe in this round. This means it can be set as the sum of the weighted standard deviation with constraints calculated for the first time in this round of process recipe and the reserved value. Again, the weighted standard deviation with constraints calculated for the first time in this round of process recipe should be compared with this initially calculated online calculated threshold, and subsequent weighted standard deviations with constraints should be compared with the new online calculated threshold.

[0065] To update the temperature deviation threshold, the weighted standard deviation with constraints calculated each time can be stored sequentially in a fixed-length array B, for example... Figure 7 As shown, after array A is filled for the first time, the calculated weighted standard deviation with constraints is stored in the first position of array B. Array A is then cleared to store the second batch of measured temperature values, and the calculation of the weighted standard deviation with constraints continues. Each time array A is filled, the calculated weighted standard deviation with constraints is stored in the next position of array B. If the calculated weighted standard deviation with constraints determines that the temperature is normal by comparing it with the temperature deviation threshold, the temperature deviation threshold is updated based on the weighted standard deviation with constraints stored in array B and the reserved value. The updated temperature deviation threshold is then used as the threshold for the next calculation.

[0066] As a first feasible implementation, the temperature deviation threshold can be updated to the sum of the weighted standard deviation with constraints and the reserved value obtained in this calculation, and used as the threshold for the next calculation.

[0067] As a first feasible implementation, if there are multiple weighted standard deviations with constraints stored in array B, the temperature deviation threshold is updated to the sum of the average of the multiple weighted standard deviations with constraints and the reserved value, which is used as the threshold for the next calculation.

[0068] It should be noted here that if an abnormal temperature is determined or the main process stage of the current process formulation is completed, the weighted standard deviations of each constraint stored in array B should be cleared.

[0069] Furthermore, after determining that the temperature is abnormal, the temperature monitoring method of this application embodiment may further include the following step S607:

[0070] S607 outputs abnormal temperature alarm information.

[0071] In this embodiment of the application, after determining that the temperature is abnormal, a temperature abnormality alarm message is output to remind the user that the measured temperature value of the monitoring point is unreliable and that if the measured temperature value of the monitoring point is used, it will affect the temperature control at that location, thereby affecting the normal process and damaging the film quality.

[0072] Furthermore, after determining that the temperature is abnormal, the temperature monitoring method of this application embodiment may further include the following step S608:

[0073] S608, obtain the backup temperature value of the corresponding monitoring point.

[0074] In this embodiment, after a temperature anomaly is identified, to minimize disruption to the process, the current process formulation can continue, using the backup temperature value of the monitoring point with the anomaly as the measured temperature value for that monitoring point for continued temperature control. Specifically, the measured temperature value of the nearest temperature measuring point can be obtained, and the sum of the measured temperature value of the nearest temperature measuring point and the compensation value can be calculated as the backup temperature value for that monitoring point. The compensation value is the temperature difference between the monitoring point and the nearest temperature measuring point during the main process stage of the previous round of normal operation (run).

[0075] S609 uses the backup temperature value of the monitoring point as the actual measured temperature value of the monitoring point for temperature control.

[0076] In this embodiment of the application, the backup temperature value of the monitoring point is used as the actual measured temperature value of the monitoring point for temperature control to ensure the process effect and complete the subsequent process steps of this round of process formula.

[0077] For example: In the previous round of normal operation of the process formula, the measured temperature value of monitoring point C is 760℃, and the measured temperature value of the temperature measuring point D closest to point C is 758℃. Then the temperature difference between point C and point D is 2℃, and this temperature difference is used as the compensation value for point C.

[0078] When an abnormal temperature alarm is output, it indicates that the measured temperature value at point C has dropped and is no longer reliable. At this time, the measured temperature value at point D is 758.1℃. The measured temperature value at point D (758.1℃) + the compensation value at point C (2℃) = 760.1℃ is used as the current temperature value of monitoring point C to replace the measured temperature value. This can ensure that the temperature at monitoring point C is close to normal to the greatest extent possible, and ensure that the temperature control of monitoring point C is normal.

[0079] It should be noted that if the number of temperature anomaly alarm messages exceeds a preset threshold, the current process formulation will be skipped to minimize its impact. Subsequent offline data analysis of the entire process formulation can be performed to determine if the temperature anomaly affects the film formation quality. If it does affect the film formation quality or uniformity, a detailed inspection of the machine is necessary to identify the cause of the problem, resolve the issue, and ensure stable machine operation.

[0080] In summary, the temperature monitoring method of this application, when calculating the weighted standard deviation with constraints, ensures that the weighted standard deviation is equal to or greater than the ordinary standard deviation because the constraints include a weighting coefficient equal to or greater than 1. Since the ordinary standard deviation amplifies the temperature deviation between multiple measured temperature values ​​compared to the temperature deviation between the measured and set temperature values ​​in related technologies, the weighted standard deviation, which is equal to or greater than the ordinary standard deviation, further amplifies the temperature deviation between multiple measured temperature values. By comparing this with a temperature deviation threshold, it can more timely and accurately monitor and capture short-term rapid temperature drops, facilitating subsequent measures to avoid affecting normal process performance or preventing process failure. The algorithm for monitoring short-term rapid temperature drops using the weighted standard deviation is only activated when the temperature field is stable, ensuring monitoring effectiveness while avoiding wasted computational resources. Based on machine usage time, the initial value of the temperature deviation threshold is set to either a default deviation threshold or an online calculation threshold, and the next calculated temperature deviation threshold is updated based on the current calculated weighted standard deviation, making the temperature deviation threshold setting more reasonable. When the temperature is abnormal, the backup temperature value of the monitoring point is used as the actual measured temperature value of the monitoring point for temperature control, so that the temperature of the monitoring point is close to normal and the temperature control of the monitoring point is normal.

[0081] This application also provides a semiconductor process apparatus. For example... Figure 1 As shown, the semiconductor process equipment includes a temperature controller, a process tube 2, and a thermocouple 7 disposed inside the process tube 2. The temperature controller is used to implement the steps of any of the temperature monitoring method embodiments described above.

[0082] In the semiconductor process equipment of this application embodiment, when calculating the weighted standard deviation with constraints, since the constraints include a weighting coefficient equal to or greater than 1, the calculated weighted standard deviation is equal to or greater than the ordinary standard deviation. Because the ordinary standard deviation, compared to the temperature deviation between the measured temperature value and the set temperature value in related technologies, can amplify the temperature deviation of multiple measured temperature values, the weighted standard deviation, equal to or greater than the ordinary standard deviation, can further amplify the temperature deviation between multiple measured temperature values. Then, by comparing it with a temperature deviation threshold, it can more timely and accurately monitor and capture short-term rapid temperature drops, facilitating subsequent measures to avoid affecting normal process performance or preventing process failure. The algorithm for monitoring short-term rapid temperature drops using weighted standard deviation is only activated when the temperature field is stable, ensuring monitoring effectiveness while avoiding wasted computational resources. Based on the equipment usage time, the initial value of the temperature deviation threshold is set to a default deviation threshold or an online calculation threshold, and the next calculated temperature deviation threshold is updated based on the weighted standard deviation calculated this time, making the temperature deviation threshold setting more reasonable. When the temperature is abnormal, the backup temperature value of the monitoring point is used as the actual measured temperature value of the monitoring point for temperature control, so that the temperature of the monitoring point is close to normal and the temperature control of the monitoring point is normal.

[0083] This application also proposes a readable storage medium storing one or more computer programs, the one or more computer programs including instructions. When the program or instructions are executed by a processor in a semiconductor process apparatus including multiple applications, the processor in the semiconductor process apparatus is able to execute the various processes of the above-described temperature monitoring method embodiments, and is specifically used to execute the steps of any of the above-described temperature monitoring method embodiments.

[0084] In the readable storage medium of this application embodiment, when calculating the weighted standard deviation with constraints, since the constraints include a weighting coefficient equal to or greater than 1, the calculated weighted standard deviation is equal to or greater than the ordinary standard deviation. Because the ordinary standard deviation, compared to the temperature deviation between the measured temperature value and the set temperature value in related technologies, can amplify the temperature deviation of multiple measured temperature values, the weighted standard deviation, equal to or greater than the ordinary standard deviation, can further amplify the temperature deviation between multiple measured temperature values. Then, by comparing it with a temperature deviation threshold, it can more timely and accurately monitor and capture short-term rapid temperature drops, facilitating subsequent measures to avoid affecting normal process effects or process failures. The algorithm for monitoring short-term rapid temperature drops using weighted standard deviation is only activated when the temperature field is stable, ensuring monitoring effectiveness while avoiding wasted computational resources. Based on the machine usage time, the initial value of the temperature deviation threshold is set to a default deviation threshold or an online calculation threshold, and the next calculated temperature deviation threshold is updated based on the weighted standard deviation calculated this time, making the temperature deviation threshold setting more reasonable. When the temperature is abnormal, the backup temperature value of the monitoring point is used as the actual measured temperature value of the monitoring point for temperature control, so that the temperature of the monitoring point is close to normal and the temperature control of the monitoring point is normal.

[0085] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0086] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0087] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0088] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] 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.

[0090] 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.

[0091] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0092] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0093] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0094] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0095] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0096] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0097] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A temperature monitoring method, characterized in that, include: Acquire multiple measured temperature values ​​from monitoring points of semiconductor process equipment; Based on the multiple measured temperature values, calculate the weighted standard deviation with constraints, wherein the constraints include a weighting coefficient equal to or greater than 1; If the weighted standard deviation with constraints is greater than the temperature deviation threshold, then a temperature anomaly is determined. If the weighted standard deviation with constraints is equal to or less than the temperature deviation threshold, then the temperature is determined to be normal. The step of calculating the weighted standard deviation with constraints based on the multiple measured temperature values ​​includes: The weighting coefficient is determined based on whether the measured temperature values ​​at multiple consecutive sampling times within the sampling period show a continuous increase or a continuous decrease, wherein the multiple sampling times are less than the number of samples within the sampling period; The weighted standard deviation with constraints is calculated based on the weighting coefficients.

2. The method according to claim 1, characterized in that, The step of calculating the weighted standard deviation with constraints based on the multiple measured temperature values ​​includes: The weighted standard deviation with constraints is calculated using the following formula: in, S The weighted standard deviation with constraints is... β The weighting coefficients are... X i For the first i The measured temperature value at each sampling time, n The number of samples within the sampling time period. X Within the sampling time n The average of the measured temperature values, where α is a preset real number greater than 1. j Less than n The natural number.

3. The method according to claim 1, characterized in that, The initial value of the temperature deviation threshold is the default deviation threshold or the sum of the weighted standard deviation with constraints obtained from the first calculation of the process formula in this round and the preset reserved value. The default deviation threshold is the sum of the offline standard deviation calculated based on the offline temperature value and the reserved value. The method further includes: Store the constrained weighted standard deviation; If the temperature is determined to be normal, the temperature deviation threshold is updated based on the stored weighted standard deviation with constraints and the reserved value.

4. The method according to claim 3, characterized in that, The step of updating the temperature deviation threshold based on the stored weighted standard deviation with constraints and the reserved value includes: If there are multiple stored weighted standard deviations with constraints, the temperature deviation threshold is updated to the sum of the average of the multiple stored weighted standard deviations with constraints and the reserved value.

5. The method according to claim 3, characterized in that, Also includes: If an abnormal temperature is determined or the main process stage of the current process formulation is completed, the stored weighted standard deviation with constraints is cleared.

6. The method according to claim 1, characterized in that, Following the determination of the temperature anomaly, the following is also included: Output temperature abnormality alarm information.

7. The method according to claim 1, characterized in that, Following the determination of the temperature anomaly, the following is also included: Obtain the backup temperature value of the corresponding monitoring point; The backup temperature value of the monitoring point is used as the actual measured temperature value of the monitoring point for temperature control.

8. The method according to claim 7, characterized in that, The step of obtaining the backup temperature value of the corresponding monitoring point includes: Obtain the measured temperature value of the nearest temperature measuring point to the corresponding monitoring point; The sum of the measured temperature value and the compensation value of the nearest temperature measuring point is calculated and used as the backup temperature value of the monitoring point. The compensation value is the temperature difference between the monitoring point and the nearest temperature measuring point in the main process stage of the previous round of normal operation.

9. The method according to claim 6, characterized in that, Also includes: If the number of temperature abnormality alarm messages exceeds a preset threshold, the process will skip steps and complete the current round of the process formulation.

10. The method according to claim 1, characterized in that, The acquisition of multiple measured temperature values ​​at monitoring points of the semiconductor process equipment includes: Determine whether the temperature field of the semiconductor process equipment is stable; If stable, then obtain multiple measured temperature values ​​from the monitoring points of the semiconductor process equipment.

11. The method according to claim 10, characterized in that, The determination of whether the temperature field of the semiconductor process equipment is stable includes: Calculate the temperature deviation between the currently measured temperature value and the set temperature value at the corresponding time. If the temperature deviation is less than a preset deviation threshold, then the temperature field of the semiconductor process equipment is determined to be stable.

12. A semiconductor process apparatus, characterized in that, The method includes a temperature controller, a process tube, and a thermocouple disposed inside the process tube, wherein the temperature controller is used to implement the steps of the method as described in any one of claims 1-11.

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