Method and system for optimizing structural parameters of trench Schottky diode

Abstract

The invention discloses a trench Schottky diode structure parameter optimization method and system, and relates to the data processing related field, and the method comprises the steps: obtaining an application scene feature set of a target trench Schottky diode; key structure parameter matching and historical application record mining are carried out; traversing the historical application record set to carry out loss calculation; retrieving the historical application record set to obtain a historical key structure parameter group set; taking the historical key structure parameter group set as an initial optimization space, and performing competitive expansion in combination with the historical loss amount set and a preset expansion scale to obtain a target optimization space; and performing key structure parameter aggregation search, and determining a target key structure parameter group as a structure parameter optimization result. The technical problems that the optimization efficiency is low and the optimization result is lack of accuracy in the existing trench Schottky diode structure parameter optimization are solved, and the technical effects of improving the optimization efficiency and ensuring the accuracy and the reliability of the optimization result are achieved.

Description

Technical Field

[0001] The present application relates to data processing related fields, and in particular to a method and system for optimizing structural parameters of a trench Schottky diode. Background Art

[0002] In the field of semiconductor devices, trench Schottky diodes, as a high-performance power semiconductor device, are widely used in high-frequency, high-voltage, and high-power power electronic systems. The optimization of their performance is crucial to improving the efficiency, stability, and reliability of the entire power electronic system. The performance of trench Schottky diodes is affected by a variety of structural parameters, such as trench depth, trench width, and the contact characteristics between the anode metal and the semiconductor material. In the existing trench Schottky diode structural parameter optimization methods, the optimal solution is usually found through repeated trials and parameter adjustments based on the experience and intuition of engineers. This method is time-consuming and labor-intensive, and the optimization results often rely on the personal experience and knowledge level of engineers, lacking systematicity and accuracy.

[0003] In the current related technologies, the optimization of the structural parameters of trench Schottky diodes has technical problems such as low optimization efficiency and lack of accuracy in the optimization results. Summary of the invention

[0004] The present application provides a method and system for optimizing the structural parameters of a trench Schottky diode, collects and analyzes application scenario characteristics of a target trench Schottky diode, matches key structural parameters and mines historical application records based on the application scenario characteristics, obtains historical key structural parameter groups and historical loss sets similar to the current application scenario, calculates the loss of historical application records using a loss objective function, and constructs an initial optimization space using the key structural parameter group as an index. The initial optimization space is competitively expanded in combination with the historical loss set and a preset expansion scale to obtain a target optimization space, performs a clustered search for key structural parameters within the target optimization space, and finds the target key structural parameter group as the final optimization result through iterative optimization, thereby achieving the technical effect of improving optimization efficiency and ensuring the accuracy and reliability of the optimization results.

[0005] The present application provides a method for optimizing the structural parameters of a trench Schottky diode, comprising: obtaining an application scenario feature set of a target trench Schottky diode; performing key structural parameter matching and historical application record mining based on the application scenario feature set to obtain a key structural parameter group and a historical application record set; using a loss objective function to traverse the historical application record set to calculate the loss amount and obtain a historical loss amount set; using the key structural parameter group as an index to retrieve the historical application record set and obtain a historical key structural parameter group set; using the historical key structural parameter group set as an initial optimization space, and competitively expanding the initial optimization space in combination with the historical loss amount set and a preset expansion scale to obtain a target optimization space; performing a key structural parameter clustering search on the target optimization space to determine a target key structural parameter group, and using the target key structural parameter group as a structural parameter optimization result.

[0006] In a possible implementation, the following processing is performed: constructing a loss objective function, wherein the loss objective function is: ;in, is the loss amount, is the on-resistance, is the reverse breakdown voltage, Switching losses, , is the weight parameter of on-resistance in calculating the loss, is the weight parameter of reverse breakdown voltage in calculating the loss, is the weight parameter of the switching loss in calculating the loss amount. In a possible implementation, the set of historical key structural parameter groups is used as the initial optimization space, and the initial optimization space is competitively expanded in combination with the historical loss amount set and the preset expansion scale to obtain the target optimization space, and the following processing is performed: extract the maximum value of the historical loss amount in the historical loss amount set, and use the historical key structural parameter group corresponding to the maximum value of the historical loss amount as the competitive expansion avoidance direction, extract the minimum value of the historical loss amount in the historical loss amount set, and use the historical key structural parameter group corresponding to the minimum value of the historical loss amount as the competitive expansion moving direction; based on the competitive expansion avoidance direction and the competitive expansion moving direction , adjusting at least one key structural parameter of the historical key structural parameter group set in the initial optimization space according to the preset expansion scale to obtain a stage competition expansion optimization space; evaluating the stage competition expansion optimization space using a competition expansion evaluation function to obtain a stage competition expansion evaluation result; judging whether the stage competition expansion evaluation result meets the preset requirements, if so, taking the stage competition expansion optimization space as the target optimization space; if not, competitively expanding the historical key structural parameter group set in the initial optimization space again, and updating the stage competition expansion optimization space according to the expansion result.

[0007] In a possible implementation, the competition expansion optimization space of the stage is evaluated by using a competition expansion evaluation function to obtain a stage competition expansion evaluation result, and the following processing is performed: obtaining the competition expansion evaluation function, wherein the competition expansion evaluation function is: ;in, Expand evaluation results for stage competition, Expand optimization space for stage competition, The number of historical key structural parameter groups in the optimization space for stage competition expansion, is a neighborhood consisting of historical key structural parameter groups whose distance to the i-th historical key structural parameter group in the stage competition expansion optimization space is within the preset distance threshold. is the number of historical key structural parameter groups contained in the neighborhood of the i-th historical key structural parameter group in the stage competition expansion optimization space, is the i-th historical key structural parameter group in the stage competition expansion optimization space, is the jth historical key structural parameter group in the neighborhood of the ith historical key structural parameter group. In a possible implementation, the target optimization space is searched for key structural parameters in an aggregated manner to determine the target key structural parameter group, and the following processing is performed: in combination with the loss objective function, the target optimization space is traversed to predict the loss amount, and a target optimization loss amount set is obtained; the minimum value of the target optimization loss amount in the target optimization loss amount set is extracted, and it is used as the initial aggregate search target; according to a preset iteration step, the initial aggregate search target is iterated in the target optimization loss amount set to obtain an aggregate search target; the key structural parameter group corresponding to the aggregate search target is used as the target key structural parameter group.

[0008] In a possible implementation, the following processing is performed: obtaining the number of loss amounts in the target optimization loss amount set whose distance to the initial clustering search target is less than or equal to the preset iteration step, and obtaining the initial clustering number; iterating the initial clustering search target according to the preset iteration step to obtain the iterative clustering search target, and obtaining the number of loss amounts in the target optimization loss amount set whose distance to the iterative clustering search target is less than or equal to the preset iteration step to obtain the iterative clustering number; judging whether the initial clustering number is less than or equal to the iterative clustering number, if so, iterating based on the iterative clustering search target until the preset number of iterations is met, and taking the iterative clustering search target obtained in the last iteration as the clustering search target. In a possible implementation, the following processing is performed: the key structural parameter group includes the trench oxide layer thickness, the active area width between trenches, and the contact angle between the trench oxide layer and the active area.

[0009] The present application also provides a trench Schottky diode structural parameter optimization system, including: an application scenario feature set acquisition module, used to obtain an application scenario feature set of a target trench Schottky diode; a matching mining module, used to perform key structural parameter matching and historical application record mining based on the application scenario feature set, and obtain a key structural parameter group and a historical application record set; a loss amount calculation module, used to use a loss objective function to traverse the historical application record set to perform loss amount calculation, and obtain a historical loss amount set; a retrieval module, used to retrieve the historical application record set with the key structural parameter group as an index, and obtain a historical key structural parameter group set; a competitive expansion module, used to use the historical key structural parameter group set as an initial optimization space, and competitively expand the initial optimization space in combination with the historical loss amount set and a preset expansion scale to obtain a target optimization space; a structural parameter optimization result acquisition module, used to perform a key structural parameter cluster search on the target optimization space, determine a target key structural parameter group, and use the target key structural parameter group as a structural parameter optimization result.

[0010] The method and system for optimizing the structural parameters of a trench Schottky diode proposed in this application first obtain an application scenario feature set of a target trench Schottky diode, then perform key structural parameter matching and historical application record mining based on the application scenario feature set to obtain a key structural parameter group and a historical application record set, then use the loss objective function to traverse the historical application record set to calculate the loss amount to obtain a historical loss amount set, then use the key structural parameter group as an index to retrieve the historical application record set to obtain a historical key structural parameter group set, then use the historical key structural parameter group set as an initial optimization space, and competitively expand the initial optimization space in combination with the historical loss amount set and a preset expansion scale to obtain a target optimization space, finally perform a key structural parameter clustering search on the target optimization space to determine the target key structural parameter group, and use the target key structural parameter group as the structural parameter optimization result, thereby achieving the technical effect of improving the optimization efficiency and ensuring the accuracy and reliability of the optimization results. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0012] Figure 1 A schematic flow chart of a method for optimizing structural parameters of a trench Schottky diode provided in an embodiment of the present application.

[0013] Figure 2 A schematic diagram of the structure of a trench Schottky diode structural parameter optimization system provided in an embodiment of the present application.

[0014] Explanation of the reference numerals: application scenario feature set acquisition module 10, matching mining module 20, loss amount calculation module 30, retrieval module 40, competition expansion module 50, structural parameter optimization result acquisition module 60. DETAILED DESCRIPTION

[0015] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0016] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0017] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0018] The present application provides a method for optimizing the structural parameters of a trench Schottky diode. Figure 1 As shown, the method includes: Step S100, obtaining an application scenario feature set of a target trench Schottky diode.

[0019] Specifically, through data collection and preprocessing technologies, such as sensor data collection, historical database query, etc., detailed information about the target trench Schottky diode application scenario is obtained, that is, the application scenario characteristics (a series of parameters or conditions that affect the performance and usage conditions of the trench Schottky diode) are determined, such as operating temperature range, current density, voltage level, packaging form, etc. Then, the specific values ​​of these characteristics are collected to form an application scenario feature set.

[0020] Step S200 , performing key structure parameter matching and historical application record mining based on the application scenario feature set to obtain a key structure parameter group and a historical application record set.

[0021] Specifically, according to the characteristics of the application scenarios, data mining and machine learning techniques, such as association rule mining and cluster analysis, are used to match key structural parameters (structural features that have an important impact on the performance of trench Schottky diodes, such as anode trench depth, cathode material type, etc.) from the historical database to form a key structural parameter group. At the same time, relevant historical application records are mined, that is, records of using trench Schottky diodes in the same or similar application scenarios in the past are found, including key structural parameters, performance, etc.

[0022] In a possible implementation, step S200 further includes step S210, wherein the key structural parameter group includes the thickness of the trench oxide layer, the width of the active area between the trenches, and the contact angle between the trench oxide layer and the active area.

[0023] Specifically, in this example, the key structural parameter group includes the thickness of the trench oxide layer, the width of the active area between the trenches, and the contact angle between the trench oxide layer and the active area. Among them, the thickness of the trench oxide layer refers to the thickness of the oxide layer inside the trench, which affects the breakdown voltage and leakage current of the diode. The width of the active area between the trenches refers to the width of the active area between adjacent trenches, which affects the current capacity and frequency characteristics of the diode. The contact angle between the trench oxide layer and the active area refers to the angle of the contact interface between the trench oxide layer and the active area, which affects the electric field distribution and current flow path of the diode. The selection of key structural parameters for this implementation method is based on a large amount of data mining and professional knowledge, so it can more accurately reflect the performance requirements of the diode, thereby making the optimized diode performance more excellent and improving the pertinence and efficiency of the optimization.

[0024] Step S300, using the loss objective function, traverse the historical application record set to calculate the loss amount and obtain a historical loss amount set.

[0025] Specifically, the loss objective function is a function used to quantify the gap between structural parameters and ideal performance. This function measures the gap between diode performance and target performance under given structural parameters. For each record in the historical application record set, the loss objective function is used to calculate its loss amount to form a historical loss amount set. The loss amount can be calculated based on factors such as performance indicator deviation and cost overrun, and is a numerical value that measures performance deviation. In a possible implementation, step S300 further includes step S310, constructing a loss objective function, wherein the loss objective function is: ;in, is the loss amount, is the on-resistance, is the reverse breakdown voltage, Switching losses, , is the weight parameter of on-resistance in calculating the loss, is the weight parameter of reverse breakdown voltage in calculating the loss, It is the weight parameter of switching loss in calculating the loss amount.

[0026] Specifically, the main performance indicators of trench Schottky diodes include on-resistance, reverse breakdown voltage and switching loss, which directly reflect the working efficiency, voltage resistance and dynamic performance of the diode. The loss amount is a comprehensive indicator used to measure the gap between the diode performance and the target performance. The loss amount is the weighted sum of the on-resistance, reverse breakdown voltage and switching loss. The weight parameter is determined by statistical analysis of historical data and is used to measure the relative importance of different performance indicators in the total loss amount. Among them, the on-resistance is the resistance from the anode to the cathode of the diode when it is forward biased, which determines the power consumption and efficiency of the diode in the on state. The reverse breakdown voltage is the maximum voltage that the diode can withstand without breakdown when it is reverse biased, reflecting the voltage resistance of the diode. The switching loss is the energy consumed by the diode during the switching process, which affects the dynamic performance and efficiency of the diode. This implementation method provides a comprehensive performance evaluation standard for the structural parameter optimization of the trench Schottky diode by constructing a loss objective function, and provides a basis for subsequent optimization steps. This implementation method not only improves the accuracy and efficiency of the optimization, but also enhances the flexibility and adaptability of the optimization results.

[0027] Step S400: using the key structure parameter group as an index, searching the historical application record set to obtain a historical key structure parameter group set.

[0028] Specifically, database retrieval techniques, such as index query, similarity matching, etc., are used to retrieve records with similar structural parameters in a historical application record set according to the key structural parameter group to form a historical key structural parameter group set.

[0029] Step S500: taking the historical key structural parameter group set as the initial optimization space, and competitively expanding the initial optimization space in combination with the historical loss amount set and a preset expansion scale to obtain a target optimization space.

[0030] Specifically, taking the set of historical key structural parameter groups as the starting point, the initial optimization space is expanded using an optimization algorithm according to the size of the loss in the historical loss set and the preset expansion scale (parameters that control the expansion speed and range of the optimization space, such as search step size, mutation rate, etc.). The optimization space is expanded by introducing a competition mechanism (such as natural selection, fitness evaluation, etc.) to form a target optimization space containing more potential optimal solutions.

[0031] In a possible implementation, the historical key structural parameter group set is used as the initial optimization space, and the initial optimization space is competitively expanded in combination with the historical loss amount set and the preset expansion scale to obtain the target optimization space. Step S500 further includes step S510, extracting the maximum historical loss amount in the historical loss amount set, using the historical key structural parameter group corresponding to the maximum historical loss amount as the competitive expansion avoidance direction, extracting the minimum historical loss amount in the historical loss amount set, and using the historical key structural parameter group corresponding to the minimum historical loss amount as the competitive expansion moving direction. Specifically, find the record with the largest loss amount from the historical loss amount set, and the corresponding historical key structural parameter group is regarded as the direction to be avoided during competitive expansion (i.e., the "avoidance direction"). Similarly, find the record with the smallest loss amount from the historical loss amount set, and the corresponding historical key structural parameter group is regarded as the direction to be approached during the optimization process (i.e., the "moving direction"). Among them, the maximum historical loss amount is the parameter configuration that caused the maximum loss in the historical application. The minimum historical loss amount is the parameter configuration that caused the minimum loss in the historical application. The competitive expansion avoidance direction is the parameter configuration direction that should be avoided during the optimization process. The competitive expansion movement direction is the parameter configuration direction that should be pursued during the optimization process.

[0032] Step S520, based on the competition expansion avoidance direction and the competition expansion moving direction, adjust at least one key structural parameter of the historical key structural parameter group set in the initial optimization space according to the preset expansion scale to obtain the stage competition expansion optimization space. Specifically, based on the determined competition expansion avoidance direction and moving direction, and the preset expansion scale (such as the range or step size of parameter adjustment), each historical key structural parameter group in the initial optimization space is adjusted. For each parameter group, at least one key structural parameter (such as groove depth, width, doping concentration, etc.) is adjusted to form a new parameter configuration. The new parameter configuration set formed after the parameter adjustment is the stage competition expansion optimization space.

[0033] Step S530, using the competition expansion evaluation function to evaluate the stage competition expansion optimization space, and obtain the stage competition expansion evaluation result. Specifically, use the competition expansion evaluation function (a function used to evaluate the quality of parameter configuration) to evaluate each parameter configuration in the stage competition expansion optimization space. For each new parameter configuration, calculate its evaluation score to obtain the stage competition expansion evaluation result. Step S540, determine whether the stage competition expansion evaluation result meets the preset requirements. If so, use the stage competition expansion optimization space as the target optimization space. Specifically, compare whether the stage competition expansion evaluation result meets the preset requirements. If the requirements are met, end the competition expansion process and use the stage competition expansion optimization space as the target optimization space. If the requirements are not met, proceed to the next step.

[0034] Step S550, if not, then the historical key structural parameter group set in the initial optimization space is competitively expanded again, and the stage competitive expansion optimization space is updated according to the expansion result. Specifically, according to the result that does not meet the preset requirements, the expansion strategy is adjusted (such as changing the expansion scale, etc.), and the initial optimization space is competitively expanded again, the expansion process is optimized, and the stage competitive expansion optimization space is updated. This implementation method uses the competitive expansion method to maintain the historical optimal parameter configuration direction while avoiding the influence of the worst parameter configuration, thereby effectively searching for a better configuration in the parameter space. Through continuous iteration and adjustment, the global optimal solution can be gradually approached to improve the performance of the trench Schottky diode.

[0035] In a possible implementation, the competition expansion optimization space of the stage is evaluated by using a competition expansion evaluation function to obtain a stage competition expansion evaluation result. Step S530 further includes step S531, obtaining the competition expansion evaluation function, wherein the competition expansion evaluation function is: ;in, Expand evaluation results for stage competition, Expand optimization space for stage competition, The number of historical key structural parameter groups in the optimization space for stage competition expansion, is a neighborhood consisting of historical key structural parameter groups whose distance to the i-th historical key structural parameter group in the stage competition expansion optimization space is within the preset distance threshold. is the number of historical key structural parameter groups contained in the neighborhood of the i-th historical key structural parameter group in the stage competition expansion optimization space, is the i-th historical key structural parameter group in the stage competition expansion optimization space, is the jth historical key structural parameter group in the neighborhood of the ith historical key structural parameter group. Specifically, the competitive expansion evaluation function is used to evaluate the overall performance of the stage competitive expansion optimization space. The function comprehensively considers the relationship between each historical key structural parameter group (i.e., candidate solution) in the optimization space, especially the similarity and difference between them. The competitive expansion evaluation function accepts a parameter, the stage competitive expansion optimization space, as input, and outputs a scalar value as the evaluation result. For each historical key structural parameter group in the optimization space, its neighborhood is calculated. The neighborhood consists of all other historical key structural parameter groups within a preset distance threshold from each historical key structural parameter group. This distance can be Euclidean distance, Manhattan distance, etc. For each historical key structural parameter group, the number of historical key structural parameter groups contained in its neighborhood is counted. Finally, according to the competitive expansion evaluation function formula, the evaluation result of the entire optimization space is calculated and output. The formula comprehensively considers the information of each historical key structural parameter group and its neighborhood to evaluate the overall performance of the optimization space. This implementation method quantitatively evaluates the overall performance of the optimization space by defining a competitive expansion evaluation function. This function takes into account the similarities and differences between parameter configurations, and helps to identify parameter configuration sets with good distribution and diversity. In the optimization process, we hope to find a parameter configuration set that can maintain a certain diversity (to avoid premature convergence to the local optimal solution) and can also reflect good performance. By continuously adjusting and optimizing this set, we can gradually approach the global optimal solution, improve the accuracy of the optimization, and enhance the robustness and stability of the optimization.

[0036] Step S600, performing a clustered search of key structural parameters on the target optimization space, determining a target key structural parameter group, and using the target key structural parameter group as a structural parameter optimization result.

[0037] Specifically, cluster analysis, local search and other algorithms are used to cluster search the key structural parameter groups in the target optimization space, that is, to find parameter groups with similar performance or characteristics, and determine the target key structural parameter group according to the performance evaluation index (such as the minimum loss), that is, the optimal structural parameter combination determined after optimization, which is used as the final result of structural parameter optimization. The embodiment of the present application collects and analyzes the application scenario characteristics of the target trench Schottky diode, matches the key structural parameters and mines the historical application records based on the application scenario characteristics, obtains the historical key structural parameter group and the historical loss amount set similar to the current application scenario, uses the loss objective function to calculate the loss amount of the historical application record, and constructs the initial optimization space with the key structural parameter group as the index, combines the historical loss amount set and the preset expansion scale, and expands the initial optimization space competitively to obtain the target optimization space, and clusters the key structural parameters in the target optimization space. The target key structural parameter group is found through iterative optimization as the final optimization result and other technical means, which achieves the technical effect of improving the optimization efficiency and ensuring the accuracy and reliability of the optimization results.

[0038] In a possible implementation, a cluster search of key structural parameters is performed on the target optimization space to determine a target key structural parameter group, and step S600 further includes step S610, combining the loss objective function, traversing the target optimization space to predict the loss amount, and obtaining a target optimization loss amount set. Specifically, using the loss objective function, traverse each key structural parameter group in the target optimization space, substitute each parameter group into the loss objective function, and calculate the corresponding loss amount. All calculated loss amounts are combined into a set, namely, the target optimization loss amount set.

[0039] Step S620, extract the minimum value of the target optimization loss in the target optimization loss set, and use it as the initial clustering search target. Specifically, find the minimum loss value in the target optimization loss set, and use the key structural parameter group corresponding to the minimum loss value as the initial clustering search target, which parameter group represents the optimal configuration in the current optimization space.

[0040] Step S630, iterate the initial clustered search target in the target optimization loss amount set according to the preset iteration step size to obtain the clustered search target. Specifically, determine the amplitude or range of parameter adjustment in each iteration, as well as the direction and method of search. Starting from the initial clustered search target, gradually adjust the parameter configuration according to the iteration step size and search strategy, and calculate the loss value corresponding to each new configuration. After each iteration, compare the loss value of the new configuration with the current clustered search target. If the new configuration is better, update the clustered search target to the new configuration.

[0041] Step S640, taking the key structural parameter group corresponding to the clustered search target as the target key structural parameter group. Specifically, when the iteration stop condition is met (such as reaching the maximum number of iterations, the loss amount no longer decreases significantly, etc.), the iterative search process ends. The key structural parameter group corresponding to the clustered search target obtained at the end of the iterative search is taken as the final optimization result. This parameter group represents the optimal solution currently searched. This implementation method selects the minimum value as the initial clustered search target, that is, starts the search from a configuration with better performance, so that a better configuration can be found more quickly. At the same time, the parameter configuration is gradually adjusted and the clustered search target is updated through iterative search, ensuring that the parameter group finally obtained is the solution with the best or approximately optimal performance under given conditions, thereby improving the accuracy of optimization.

[0042] In a possible implementation, step S630 further includes step S631, obtaining the number of losses in the target optimization loss set whose distance to the initial aggregation search target is less than or equal to the preset iteration step, and obtaining the initial aggregation number. Specifically, the Euclidean distance is used to calculate the distance between each loss in the target optimization loss set and the initial aggregation search target. The target optimization loss set is traversed, and the distance between each loss and the initial aggregation search target is calculated. The number of losses whose distance is less than or equal to the preset iteration step is counted, and this number is the initial aggregation number. It indicates how many losses are relatively close around the initial aggregation search target.

[0043] Step S632, iterate the initial clustered search target according to the preset iteration step to obtain an iterative clustered search target, and obtain the number of losses in the target optimization loss set whose distance to the iterative clustered search target is less than or equal to the preset iteration step to obtain an iterative clustering number. Specifically, based on the preset iteration step and random search strategy, iteratively adjust the initial clustered search target to obtain a new iterative clustering search target. Similar to step S631, calculate the distance between each loss in the target optimization loss set and the new iterative clustering search target. Count the number of losses whose distance is less than or equal to the preset iteration step to obtain the iterative clustering number. The iterative clustering search target is a new parameter configuration obtained through iterative search, which represents a candidate optimal solution in the current search process.

[0044] Step S633, determine whether the initial aggregation number is less than or equal to the iterative aggregation number. If so, iterate based on the iterative aggregation search target until the preset number of iterations is met, and use the iterative aggregation search target obtained in the last iteration as the aggregation search target. Specifically, compare the initial aggregation number and the iterative aggregation number to determine whether it is necessary to continue the iterative search. If the iterative aggregation number is greater than or equal to the initial aggregation number, it means that a better parameter configuration area has been found through iterative search, and the next iteration is performed based on the current iterative aggregation search target. If the number of iterations reaches the preset value or the iterative aggregation number no longer increases significantly, the iteration is stopped. When the iteration stops, the iterative aggregation search target obtained in the last iteration is used as the final aggregation search target. This implementation guides the iterative search process by comparing the initial aggregation number and the iterative aggregation number, and encourages continued searching in this area in order to find a better solution. This strategy not only ensures the efficiency of the search, but also increases the possibility of finding the global optimal solution.

[0045] In the above, refer to Figure 1 The method for optimizing the structural parameters of a trench Schottky diode according to an embodiment of the present invention is described in detail. Figure 2 A system for optimizing structural parameters of a trench Schottky diode according to an embodiment of the present invention is described.

[0046] The trench Schottky diode structural parameter optimization system according to the embodiment of the present invention is used to solve the technical problems of low optimization efficiency and lack of accuracy of optimization results in the existing trench Schottky diode structural parameter optimization, so as to achieve the technical effect of improving optimization efficiency and ensuring the accuracy and reliability of optimization results. The trench Schottky diode structural parameter optimization system includes: an application scenario feature set acquisition module 10, a matching mining module 20, a loss amount calculation module 30, a retrieval module 40, a competition expansion module 50, and a structural parameter optimization result acquisition module 60.

[0047] An application scenario feature set acquisition module 10 is used to acquire an application scenario feature set of a target trench Schottky diode; a matching mining module 20 is used to perform key structural parameter matching and historical application record mining based on the application scenario feature set to obtain a key structural parameter group and a historical application record set; a loss amount calculation module 30 is used to use a loss objective function to traverse the historical application record set to perform loss amount calculation to obtain a historical loss amount set; a retrieval module 40 is used to retrieve the historical application record set with the key structural parameter group as an index to obtain a historical key structural parameter group set; a competitive expansion module 50 is used to use the historical key structural parameter group set as an initial optimization space, and competitively expand the initial optimization space in combination with the historical loss amount set and a preset expansion scale to obtain a target optimization space; a structural parameter optimization result acquisition module 60 is used to perform a key structural parameter cluster search on the target optimization space, determine a target key structural parameter group, and use the target key structural parameter group as a structural parameter optimization result.

[0048] The specific configuration of the loss amount calculation module 30 will be described in detail below. As described above, the loss amount calculation module 30 may further include: a loss objective function construction unit for constructing a loss objective function, wherein the loss objective function is: ;in, is the loss amount, is the on-resistance, is the reverse breakdown voltage, Switching losses, , is the weight parameter of on-resistance in calculating the loss, is the weight parameter of reverse breakdown voltage in calculating the loss, It is the weight parameter of the switching loss when calculating the loss amount. The specific configuration of the competitive expansion module 50 will be described in detail below. As described above, the set of historical key structural parameter groups is used as the initial optimization space, and the initial optimization space is competitively expanded in combination with the historical loss amount set and the preset expansion scale to obtain the target optimization space. The competitive expansion module 50 may further include: a competitive expansion direction setting unit is used to extract the maximum value of the historical loss amount in the historical loss amount set, and use the historical key structural parameter group corresponding to the maximum value of the historical loss amount as the competitive expansion avoidance direction, extract the minimum value of the historical loss amount in the historical loss amount set, and use the historical key structural parameter group corresponding to the minimum value of the historical loss amount as the competitive expansion moving direction; the key structural parameter adjustment unit is used to adjust the competitive expansion avoidance direction based on the competitive expansion avoidance direction and the competitive expansion moving direction. According to the preset expansion scale, at least one key structural parameter of the historical key structural parameter group set in the initial optimization space is adjusted to obtain a stage competition expansion optimization space; the evaluation unit is used to evaluate the stage competition expansion optimization space using a competition expansion evaluation function to obtain a stage competition expansion evaluation result; the target optimization space acquisition unit is used to determine whether the stage competition expansion evaluation result meets the preset requirements, and if so, the stage competition expansion optimization space is used as the target optimization space; the stage competition expansion optimization space update unit is used to competitively expand the historical key structural parameter group set in the initial optimization space again if not, and update the stage competition expansion optimization space according to the expansion result.

[0049] The competition expansion optimization space of the stage is evaluated by using the competition expansion evaluation function to obtain the stage competition expansion evaluation result. The evaluation unit may further include: a competition expansion evaluation function acquisition subunit for acquiring the competition expansion evaluation function, wherein the competition expansion evaluation function is: ;in, Expand evaluation results for stage competition, Expand optimization space for stage competition, The number of historical key structural parameter groups in the optimization space for stage competition expansion, is a neighborhood consisting of historical key structural parameter groups whose distance to the i-th historical key structural parameter group in the stage competition expansion optimization space is within the preset distance threshold. is the number of historical key structural parameter groups contained in the neighborhood of the i-th historical key structural parameter group in the stage competition expansion optimization space, is the i-th historical key structural parameter group in the stage competition expansion optimization space, is the jth historical key structural parameter group in the neighborhood of the ith historical key structural parameter group. Below, the specific configuration of the structural parameter optimization result acquisition module 60 will be described in detail. As described above, the target optimization space is subjected to a clustered search of key structural parameters to determine the target key structural parameter group. The structural parameter optimization result acquisition module 60 may further include: a loss amount prediction unit is used to traverse the target optimization space in combination with the loss objective function to perform loss amount prediction and obtain a target optimization loss amount set; a maximum value extraction unit is used to extract the minimum value of the target optimization loss amount in the target optimization loss amount set and use it as the initial clustered search target; an iteration unit is used to iterate the initial clustered search target in the target optimization loss amount set according to a preset iteration step to obtain a clustered search target; a target key structural parameter group acquisition unit is used to use the key structural parameter group corresponding to the clustered search target as the target key structural parameter group.

[0050] Among them, the iteration unit may further include: an initial aggregation quantity acquisition subunit is used to obtain the number of losses in the target optimization loss quantity set whose distance to the initial aggregation search target is less than or equal to the preset iteration step, and obtain the initial aggregation quantity; an iterative aggregation quantity acquisition subunit is used to iterate the initial aggregation search target according to the preset iteration step, obtain the iterative aggregation search target, and obtain the number of losses in the target optimization loss quantity set whose distance to the iterative aggregation search target is less than or equal to the preset iteration step, and obtain the iterative aggregation quantity; the aggregation search target acquisition subunit is used to determine whether the initial aggregation quantity is less than or equal to the iterative aggregation quantity. If so, iterate based on the iterative aggregation search target until the preset number of iterations is met, and take the iterative aggregation search target obtained in the last iteration as the aggregation search target.

[0051] The specific configuration of the matching mining module 20 will be described in detail below. As described above, the matching mining module 20 may further include: a key structure parameter group construction unit for constructing a key structure parameter group, the key structure parameter group including the trench oxide layer thickness, the width of the active area between the trenches, and the contact angle between the trench oxide layer and the active area.

[0052] The trench Schottky diode structural parameter optimization system provided in the embodiment of the present invention can execute the trench Schottky diode structural parameter optimization method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0053] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server, and the various units and modules included are only divided according to the functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The above specific implementation does not constitute a limitation on the scope of protection of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of protection of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and the desired results can still be achieved. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for optimizing structural parameters of a trench Schottky diode, characterized in that: The method comprises: Obtain an application scenario feature set of a target trench Schottky diode; Based on the application scenario feature set, key structural parameter matching and historical application record mining are performed to obtain a key structural parameter group and a historical application record set; Using the loss objective function, traverse the historical application record set to calculate the loss amount, and obtain a historical loss amount set; Using the key structure parameter group as an index, searching the historical application record set to obtain a historical key structure parameter group set; Taking the historical key structural parameter group set as the initial optimization space, and combining the historical loss amount set and the preset expansion scale to competitively expand the initial optimization space, to obtain the target optimization space; A key structural parameter cluster search is performed on the target optimization space to determine a target key structural parameter group, and the target key structural parameter group is used as a structural parameter optimization result.

2. The method for optimizing the structural parameters of a trench Schottky diode according to claim 1, wherein: include: Construct a loss objective function, wherein the loss objective function is: ;in, is the loss amount, is the on-resistance, is the reverse breakdown voltage, Switching losses, , is the weight parameter of on-resistance in calculating the loss, is the weight parameter of reverse breakdown voltage in calculating the loss, It is the weight parameter of switching loss in calculating the loss amount.

3. The method for optimizing the structural parameters of a trench Schottky diode according to claim 1, wherein: The historical key structural parameter group set is used as the initial optimization space, and the initial optimization space is competitively expanded in combination with the historical loss amount set and the preset expansion scale to obtain the target optimization space, including: Extracting the maximum value of the historical loss amount in the historical loss amount set, and using the historical key structural parameter group corresponding to the maximum value of the historical loss amount as the competition expansion avoidance direction; extracting the minimum value of the historical loss amount in the historical loss amount set, and using the historical key structural parameter group corresponding to the minimum value of the historical loss amount as the competition expansion movement direction; Based on the competition expansion avoidance direction and the competition expansion movement direction, adjusting at least one key structural parameter of the historical key structural parameter group set in the initial optimization space according to the preset expansion scale to obtain a stage competition expansion optimization space; Using a competition expansion evaluation function to evaluate the stage competition expansion optimization space, and obtaining a stage competition expansion evaluation result; Determine whether the stage competition expansion evaluation result meets the preset requirements, and if so, use the stage competition expansion optimization space as the target optimization space; If not, the historical key structural parameter group set in the initial optimization space is competitively expanded again, and the stage competitive expanded optimization space is updated according to the expansion result.

4. The method for optimizing the structural parameters of a trench Schottky diode according to claim 3, wherein: The competition expansion optimization space of the stage is evaluated by using the competition expansion evaluation function to obtain the stage competition expansion evaluation result, including: Obtain the competition expansion evaluation function, wherein the competition expansion evaluation function is: ;in, Expand evaluation results for stage competition, Expand optimization space for stage competition, The number of historical key structural parameter groups in the optimization space for stage competition expansion, is a neighborhood consisting of historical key structural parameter groups whose distance to the i-th historical key structural parameter group in the stage competition expansion optimization space is within the preset distance threshold. is the number of historical key structural parameter groups contained in the neighborhood of the i-th historical key structural parameter group in the stage competition expansion optimization space, is the i-th historical key structural parameter group in the stage competition expansion optimization space, is the jth historical key structural parameter group in the neighborhood of the i-th historical key structural parameter group.

5. The method for optimizing the structural parameters of a trench Schottky diode according to claim 1, wherein: Performing a clustered search of key structural parameters on the target optimization space to determine a target key structural parameter group includes: In combination with the loss objective function, the target optimization space is traversed to predict the loss amount, and a target optimization loss amount set is obtained; Extracting the minimum value of the target optimization loss amount in the target optimization loss amount set, and using it as the initial clustering search target; Iterating the initial clustered search target in the target optimization loss amount set according to a preset iteration step to obtain a clustered search target; The key structural parameter group corresponding to the clustered search target is used as the target key structural parameter group.

6. The method for optimizing the structural parameters of a trench Schottky diode according to claim 5, characterized in that: include: Obtain the number of losses in the target optimization loss set whose distance to the initial clustering search target is less than or equal to the preset iteration step length, and obtain the initial clustering number; Iterate the initial clustered search target according to the preset iteration step to obtain an iterative clustered search target, and obtain the number of loss amounts in the target optimization loss amount set whose distance to the iterative clustered search target is less than or equal to the preset iteration step to obtain the iterative clustering number; Determine whether the initial clustering number is less than or equal to the iterative clustering number. If so, iterate based on the iterative clustering search target until a preset number of iterations is met, and use the iterative clustering search target obtained in the last iteration as the clustering search target.

7. The method for optimizing the structural parameters of a trench Schottky diode according to claim 1, wherein: The key structural parameter group includes the thickness of the trench oxide layer, the width of the active area between the trenches and the contact angle between the trench oxide layer and the active area.

8. Trench Schottky diode structural parameter optimization system, characterized in that: The system is used to implement the method for optimizing the structural parameters of a trench Schottky diode according to any one of claims 1 to 7, and the system comprises: An application scenario feature set acquisition module, used to acquire an application scenario feature set of a target trench Schottky diode; A matching and mining module, used to perform key structural parameter matching and historical application record mining based on the application scenario feature set to obtain a key structural parameter group and a historical application record set; A loss amount calculation module, used to use a loss objective function to traverse the historical application record set to calculate the loss amount and obtain a historical loss amount set; A retrieval module, used to retrieve the historical application record set by taking the key structure parameter group as an index to obtain a historical key structure parameter group set; A competitive expansion module, used to use the historical key structural parameter group set as the initial optimization space, and competitively expand the initial optimization space in combination with the historical loss amount set and a preset expansion scale to obtain a target optimization space; The structural parameter optimization result acquisition module is used to perform a clustered search of key structural parameters on the target optimization space, determine a target key structural parameter group, and use the target key structural parameter group as a structural parameter optimization result.

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