Transformer screening method for semiconductor device power supply, storage medium and electronic device

Abstract

This application discloses a method for selecting transformers for semiconductor device power supplies. The method includes: receiving power circuit specification information; determining a set of key parameters in the area product method calculation formula based on the power circuit specification information; calculating the area product value AP based on the key parameter set; retrieving and filtering all candidate magnetic core models with an area product value AP greater than or equal to the calculated AP from a pre-built magnetic core database, and generating a candidate list; performing performance simulation verification based on electromagnetic fields and thermal circuits for each magnetic core model in the candidate list; and ranking the verified candidate magnetic cores according to preset optimization objectives, and outputting a list of verified candidate magnetic cores and related candidate transformers. This application organically integrates dynamic collaborative decision-making of key engineering parameters, automated performance verification based on accurate physical models, and intelligent ranking oriented towards multiple objectives, achieving objectivity and standardization in selection, and improving design efficiency and first-time success rate.

Description

Technical Field

[0001] This application relates to the field of transformer technology, specifically to a transformer selection method, storage medium, and electronic device for semiconductor device power supplies. Background Technology

[0002] The power supply system is the core driving force for the stable operation of semiconductor equipment. Among them, the power transformer is a key component for power conversion, isolation and voltage matching. The rationality of its selection has a decisive impact on the voltage withstand performance, efficiency, electromagnetic compatibility and long-term stability of the power supply system.

[0003] Currently, the initial selection of transformers in the industry generally relies on the personal experience of design engineers and a series of discrete engineering estimates. Typically, engineers need to rely on basic electrical specifications (such as power and frequency), combined with their understanding of the characteristics of magnetic core materials, winding processes and heat dissipation conditions, manually consult a large number of magnetic core datasheets, make an initial judgment based on experience and select several potentially applicable magnetic core models, and finally determine the transformer equipped with the corresponding magnetic core model.

[0004] This traditional method has inherent technical limitations. First, the process relies heavily on the individual skills and experience of engineers. For several key engineering parameters that affect the selection of magnetic cores (e.g., coefficients reflecting window space utilization, current density coefficients determining the current-carrying capacity of conductors, magnetic flux density and its variation coefficients at the set magnetic field operating point), their values ​​are often selected in a static and isolated manner based on fuzzy design criteria or past conservative cases. This results in a lack of objective and unified standards in the selection process, and the results may be overly conservative, leading to a waste of materials and space, or they may lead to failure in subsequent performance verification due to underestimation.

[0005] In addition, semiconductor equipment power systems must meet stringent operating requirements such as high voltage withstand, low interference, low fault tolerance, and process consistency. However, manual screening can lead to low accuracy in the screening results, which not only makes it difficult to match the high reliability and precision control requirements of semiconductor equipment for power systems, but may also cause fluctuations in the stability of the power system, indirectly affecting the yield and process consistency of chip manufacturing. Summary of the Invention

[0006] This invention provides a method for selecting transformers for semiconductor device power supplies, a storage medium, and an electronic device. It establishes a standard and objective automatic transformer selection process, effectively overcoming the over-reliance on personal experience and the limitations of traditional methods. It achieves objectivity and standardization in the selection process, improves design efficiency and first-time success rate, and ensures optimal matching of selected transformers in terms of electrical performance, thermal performance, and geometric dimensions, thereby solving the aforementioned technical problems.

[0007] The technical solution to the above problems in this application is: a method for screening transformers for semiconductor device power supplies, comprising: receiving power supply circuit specification information; wherein the power supply circuit specification information includes at least: rated output power Po, topology type, and operating frequency. The efficiency target η and temperature rise limit ΔT; based on the power circuit index information, determine the key parameter set of the transformer area product method calculation formula; the key parameter set includes at least: waveform factor. Window utilization coefficient Current density coefficient Working magnetic flux density Magnetic flux ripple coefficient Core loss index The winding structure coefficient ξ is calculated; the area product value AP is calculated based on the key parameter set; from the pre-built magnetic core database, all candidate magnetic core models that are greater than or equal to the calculated area product value AP are retrieved and screened, and a candidate list is generated; for each magnetic core model in the candidate list, performance simulation verification based on electromagnetic field and thermal circuit is performed, and the performance includes at least: winding loss, magnetic core loss, total loss and hot spot temperature rise; according to the preset optimization target, the candidate magnetic cores that pass the verification are sorted, and the list of candidate magnetic cores that pass the verification and the related candidate transformer list are output.

[0008] The transformer selection method for semiconductor device power supplies provided in this application effectively overcomes the over-reliance on personal experience and its limitations by constructing a fully automated process from demand input to result output. This method realizes dynamic collaborative decision-making of key engineering parameters, automated performance verification based on accurate physical models, and intelligent sorting oriented towards multiple objectives. It achieves objectification and standardization of the transformer selection process, improves design efficiency and first-time success rate, ensures optimal matching of selected transformers in terms of electrical performance, thermal performance, and geometric dimensions, and avoids performance failure or material waste caused by improper parameter selection.

[0009] In some embodiments, the key parameter set for the transformer area product method calculation formula is determined based on power circuit index information, including determining the waveform factor based on the topology type. Based on the topology type and temperature rise limit ΔT, a preset parameter relationship model is invoked to calculate the window utilization coefficient that matches the power supply circuit specifications. Current density coefficient and core loss index Based on the topology type, the flux ripple coefficient is matched from a pre-defined rule base. The value range; based on the topology type and the preset core material grade, query the core database for the preset core material at the operating frequency. Based on the recommended saturation flux density and loss curve, and the requirement to ensure loss and saturation margin, the working flux density is determined. The safe values ​​are determined by iterative optimization based on the efficiency target η and the temperature rise limit ΔT, combined with the pre-set loss curve of the magnetic core material, to determine a set of working magnetic flux densities that satisfy both loss and thermal constraints. and flux ripple coefficient The initial combination is determined; based on the topology type, the winding structure coefficient ξ, which reflects the total copper loss relationship, is calculated.

[0010] This application introduces a pre-built model and rule base trained on historical data to dynamically and collaboratively determine key parameters, transforming static parameter selection that relies on personal experience into data-driven intelligent optimization decisions. This improves the scientificity and accuracy of parameter selection, ensures the inherent matching between parameters, and thus enhances the rationality of the transformer selection process.

[0011] In some embodiments, the method further includes: if all candidate magnetic cores fail performance simulation verification, or the recommended results do not meet the constraints, then adjusting the key parameter set and returning the step of calculating the area product value AP based on the determined key parameter set.

[0012] The closed-loop iterative optimization mechanism added in this application enables the selection of power transformers for semiconductor equipment to have adaptive capabilities. When the initial solution fails to meet the requirements, the system can automatically analyze the reasons and adjust the strategy, simulate the problem-solving process of experts, and seek optimization again. This enhances the robustness of this application in the face of complex constraints and improves the success rate of finally obtaining feasible or even optimal solutions.

[0013] In some embodiments, the magnetic core database is configured as a standardized database comprising digital records of various types of magnetic cores; wherein each magnetic core record includes at least: an effective cross-sectional area. Window area Area product value AP, saturation magnetic flux density, loss parameters, and thermal resistance.

[0014] This application defines a standardized magnetic core database structure, transforming scattered, unstructured manufacturer manual data into unified, machine-readable digital information. This provides a data foundation for subsequent automatic retrieval and performance simulation, improving data utilization efficiency and the reliability of the selection process.

[0015] In some embodiments, performance simulation verification based on electromagnetic fields and thermal paths includes: according to the candidate magnetic core... and Based on the power circuit specifications, the number of turns, wire diameter, and winding structure of the winding are calculated. The winding loss is calculated based on the calculated number of turns, wire diameter, and winding structure. The core loss is calculated based on the core loss parameters obtained from the core database. The sum of the winding loss and the core loss is taken as the total core loss. The total core loss is taken as the heat source. A thermal network model is established based on the thermal resistance of the core and the preset heat dissipation conditions to obtain the steady-state hot spot temperature rise.

[0016] This application clarifies the specific technical path for performance simulation verification, standardizes and automates the complex electro-magnetic-thermal multiphysics evaluation process, and enables the batch, rapid and accurate prediction of the actual performance of each candidate magnetic core in the early stages of design by automatically executing the entire set of analyses from winding design to temperature rise calculation. This avoids the disruptive risks in the later stages caused by the disconnect between selection and verification in traditional methods.

[0017] In some embodiments, winding losses are calculated based on the calculated number of winding turns, wire diameter, and winding structure; including: if the operating frequency... The frequency exceeds a preset threshold, and / or the wire diameter estimated based on the calculated wire diameter is greater than or equal to the operating frequency. If the skin depth δ of the conductor is a preset multiple, the winding loss is calculated using the skin effect and proximity effect models; otherwise, the winding loss is calculated based on the DC resistance and effective current value of the winding structure.

[0018] This application intelligently selects the appropriate loss calculation model by comprehensively considering the ratio of frequency threshold, conductor size and skin depth. This ensures that the winding loss calculation can balance efficiency and accuracy under different operating conditions, avoiding unnecessary complex calculations in low-frequency thin-wire scenarios, while ensuring the rigor of calculations in high-frequency or thick-wire scenarios.

[0019] In some embodiments, the formula for calculating the area product value AP includes: .

[0020] Where η is the efficiency target in the power supply circuit specification information, Po is the rated output power in the power supply circuit specification, and ξ is the winding structure coefficient in the key parameter set. This refers to the core loss index within the set of key parameters. The waveform factor in the set of key parameters, D, is the duty cycle determined based on the topology type and the input-output voltage relationship. The window utilization coefficient is the key parameter set. The current density coefficient is the value in the set of key parameters. The operating magnetic flux density is defined in the set of key parameters. The flux ripple coefficient is the value in the set of key parameters. The operating frequency is the parameter information of the power supply circuit.

[0021] The formula in this application integrates electrical, magnetic, and thermal constraints, and the calculated AP value is a precise requirement that includes performance prediction, thereby improving the targeting and effectiveness of the screening.

[0022] In some embodiments, the candidate magnetic cores that have passed verification are sorted according to a preset optimization objective, including: obtaining the volume, total loss, and cost quantification values ​​of each candidate magnetic core scheme that has passed verification; performing weighted normalization processing on the quantification values ​​of volume, total loss, and cost according to preset weight coefficients, and calculating the comprehensive score of each scheme; and sorting all schemes in descending order based on the comprehensive score to generate a recommended list of candidate magnetic cores that have passed verification and a list of related candidate transformers.

[0023] This application can automatically and comprehensively select the optimal solution based on the user's preferences for different objectives (volume, loss, cost), realizing the transformation from finding a feasible solution to finding the optimal solution, so as to further support design decisions.

[0024] The present invention also provides a storage medium storing a computer program configured to execute any of the methods described in the foregoing invention.

[0025] This application can be separated from a specific development environment and become a technology product that can be independently distributed, copied and widely deployed, which expands the applicability and ease of promotion of this application, enabling any user with general computing equipment to implement this advanced transformer screening process.

[0026] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein when the processor executes the computer program, it implements a method for executing any segment of the foregoing invention.

[0027] This application combines the transformer screening method for semiconductor device power supplies with specific hardware (memory, processor) to define a dedicated or general-purpose electronic device for implementing the method, thereby forming a complete and operational physical entity system and enhancing the industrial applicability of the technical solution.

[0028] The beneficial effects of this application are: This application discloses a transformer selection method for semiconductor device power supplies. By dynamically coordinating decision-making of key engineering parameters, automating performance verification based on accurate physical models, and intelligent sorting oriented towards multiple objectives, the method achieves objectivity and standardization in the selection process, improves design efficiency and first-time success rate, ensures optimal matching of selected transformers in terms of electrical performance, thermal performance, and geometric dimensions, and avoids performance failures or material waste caused by improper parameter selection. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 A flowchart illustrating a transformer selection method for semiconductor device power supplies in some embodiments of this application; Figure 2 A flowchart illustrating the key parameter set in the area product method calculation formula for determining the transformer in some embodiments of this application; Figure 3 This is a flowchart illustrating the performance simulation verification based on electromagnetic fields and thermal circuits in some embodiments of this application; Figure 4 This is a flowchart illustrating the sorting of verified candidate magnetic cores in some embodiments of this application; Figure 5 This is a schematic diagram of the structure of the transformer screening method for semiconductor device power supply in some embodiments of this application when deployed locally. Figure 6 This is a schematic diagram of the structure of the transformer screening method for semiconductor device power supply in some embodiments of this application when deployed in a client / server manner; Figure 7 This is a schematic diagram of the structure of the transformer selection method for semiconductor device power supply in some embodiments of this application when deployed in the cloud. Figure 8 This is a schematic diagram of the structure of the transformer screening method for semiconductor device power supply in some embodiments of this application when deployed in an embedded integration manner; Figure 9 This is a schematic diagram of the structure of an electronic device in some embodiments of this application. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0032] It should be noted that, unless there is a conflict, the various features in the embodiments of this application can be combined with each other, all of which are within the protection scope of this application. Furthermore, although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than the module division in the device or the order in the flowchart. Moreover, the terms "first," "second," and "third" used in this application do not limit the data or execution order, but only distinguish identical or similar items with essentially the same function and effect.

[0033] First, semiconductor manufacturing processes demand near-perfect consistency in equipment components, a requirement directly reflected in the selection criteria for power transformers. Specifically, semiconductor equipment requires "high batch consistency," meaning that the deviations in key performance parameters of the same model transformer across different production batches (e.g., batches produced in different months, or the same model produced on different production lines) are strictly controlled within an extremely small range, and that after installation in the semiconductor equipment's power system, it achieves completely consistent performance. Poor batch-to-batch consistency in transformers—for example, the first batch having low losses and stable withstand voltage, while the second batch has higher losses and greater temperature drift—will directly lead to fluctuations in the stability of the power supply's output voltage and current. These fluctuations will further propagate to core semiconductor processes: plasma state shifts, electrostatic chuck adhesion deviations, ultimately resulting in uneven chip dimensions, device performance differences, and in severe cases, a decrease in the overall wafer yield. Conversely, if the transformer has high batch-to-batch consistency and the performance parameters of all batches of products strictly meet the standards, the power system can stably output the precise energy required by the process, ensuring that the manufacturing conditions of each batch of chips are completely uniform, thereby supporting the semiconductor production line to continuously and stably produce high-performance, high-reliability chip products.

[0034] Secondly, please refer to Figure 1 This application provides a method for screening transformers in a semiconductor device power supply, the method comprising: S1: Receives power circuit specifications from the user; power circuit specifications include at least: rated output power Po, topology type, and operating frequency. Efficiency target η and temperature rise limit ΔT; S2: Determine the set of key parameters in the transformer area product method calculation formula based on power supply circuit index information; the set of key parameters should include at least: waveform factor. Window utilization coefficient Current density coefficient Working magnetic flux density Magnetic flux ripple coefficient Core loss index , winding structure coefficient ξ; S3: Calculate the area product value AP based on the key parameter set; S4: Retrieve and select all candidate core models from the pre-built core database that are greater than or equal to the calculated area product value AP, and generate a candidate list; S5: For each magnetic core model in the candidate list, perform performance simulation verification based on electromagnetic field and thermal path. The performance should include at least: winding loss, magnetic core loss, total loss and hot spot temperature rise. S6: Based on the preset optimization target, sort the verified candidate magnetic cores and output the list of verified candidate magnetic cores and the related candidate transformer list.

[0035] Specifically, please refer to Figure 1 In step S1, the power circuit indicator information refers to a set of technical parameters describing the application scenarios and performance targets of the transformer. The methods for obtaining this information are diverse, catering to different users' operating habits and engineering scenarios. In this embodiment, the user can input the power circuit indicator information through a graphical user interface (e.g., a dedicated computer software window, a web application's operation panel, or a functional plugin integrated into circuit design software). This power circuit indicator information includes at least the transformer's topology type (e.g., flyback, forward, or full-bridge), the required rated output power Po, and the circuit's operating frequency. The software module receives the overall efficiency target η and the maximum allowable temperature rise limit ΔT from the power circuit parameters and then enters the intelligent decision-making and calculation process.

[0036] In another implementation, the performance indicators can be intelligently recommended by the software module based on preset "application scenarios." For example, when a user selects "65W USB-PD fast charging adapter" as the design task, the software module automatically loads matching initial indicators: flyback topology, 65W power, 100kHz frequency, 94% efficiency target, and 50°C temperature rise limit. The user can then fine-tune these indicators to form the final performance information.

[0037] In another implementation, the power circuit specifications are obtained from an external system via a data interface. For example, this method can be integrated with circuit design software (such as Altium Designer), directly reading the transformer parameters of the completed schematic design in the software via an API interface and using them as the specification information input; or, it can retrieve the product specification data of the current R&D project from an enterprise-level product lifecycle management system to achieve seamless integration of design specifications.

[0038] Furthermore, the power circuit specifications can also be obtained by importing historical case data. The system has a built-in library of successful historical design cases. Users can select past cases similar to the current task, and the system will automatically load the design specifications of the case as initial information, allowing users to modify them as needed for a new round of screening.

[0039] The aforementioned multiple acquisition methods enable this method to flexibly adapt to different application scenarios, from manual input to automated integration, significantly improving its versatility and ease of use.

[0040] In step S2, firstly, the software module directly determines the corresponding waveform factor based on the topology type selected by the user. For example, when the topology is resonant (such as LLC), the primary voltage is approximately a sine wave, taking... =4.44, when the topology is pulse width modulation type (such as flyback, forward, push-pull, bridge), the primary voltage is a rectangular wave, take =4.0×D, where D is the duty cycle, which is determined by the input-output voltage relationship.

[0041] Subsequently, the software module invokes a pre-defined parameter relationship model, which is trained using machine learning regression analysis based on a historical successful design case library of transformer core selection (this library can be understood as a database including relevant parameters and corresponding power circuit indicators of the best or better transformer core models obtained through traditional manual selection methods). This parameter relationship model comprehensively considers the current topology type and the strict temperature rise limit ΔT, dynamically calculating the window utilization coefficient that best matches it. Current density coefficient and core loss index This ensures that these parameters serve specific thermal performance and efficiency goals from the outset; simultaneously, the software module matches the flux ripple coefficient from another pre-defined rule base based on the topology type. The reasonable range of values ​​for the working magnetic flux density; The software module queries the magnetic core database in real time for the specified operating frequency of the material based on the user-preset magnetic core material grade. The loss characteristic curve and saturation flux density were obtained, and based on the principle of ensuring that the loss is within an acceptable range and leaving a saturation margin, a safe operating flux density was determined. The initial value of .

[0042] To further optimize, the software module will use the efficiency target η and the temperature rise limit ΔT as constraints, combined with the material loss curve, and solve through an iterative optimization algorithm to obtain a set of results that are more balanced in terms of loss and thermal constraints. and .

[0043] Finally, the software module calculates the winding structure coefficient ξ, which accurately reflects the total copper loss distribution, based on the typical current waveform and winding configuration relationship of the selected topology.

[0044] At this point, all key parameters (waveform factor) are... Window utilization coefficient Current density coefficient Working magnetic flux density Magnetic flux ripple coefficient Core loss index The winding structure coefficients (ξ) are all dynamically and collaboratively determined.

[0045] In step S3, after obtaining the complete set of key parameters, the software module substitutes them into the core calculation formula of the area product method to automatically calculate the minimum area product value AP required to meet the current power circuit index information. This calculation formula integrates electrical, magnetic and thermal constraints, and its specific form is as described above. The calculated AP value is the size threshold for screening magnetic cores.

[0046] In step S4, the software module uses the AP value as a reference to query the magnetic core database. This magnetic core database integrates digital records of mainstream magnetic core models on the market, and each record contains at least the effective cross-sectional area corresponding to each type of magnetic core. Window area The software module retrieves and initially selects all core models whose actual AP values ​​are greater than or equal to the calculated AP value, based on the calculated AP value, material saturation flux density, parameters characterizing loss characteristics, and thermal resistance parameters.

[0047] In step S5, for each magnetic core model that enters the candidate list, the software module will initiate an automated performance simulation verification process based on electromagnetic fields and thermal circuits; this process first determines the specific magnetic core's... and In conjunction with the user's power circuit specifications, the software module automatically calculates and optimizes the number of winding turns, wire diameter, and winding structure. Subsequently, when calculating winding losses, the software module intelligently determines whether the skin effect needs to be considered under the current operating frequency and wire diameter conditions, and selects to use either a high-frequency loss model or a DC resistance model for calculation accordingly. The core loss is calculated directly using the loss parameters of the core model obtained from the core database. The winding loss is then added to the core loss to obtain the total loss.

[0048] Finally, the software module uses this total loss as a heat source, combines the thermal resistance parameters of the magnetic core obtained from the magnetic core database with the preset heat dissipation conditions, establishes a thermal network model, and calculates the estimated hot spot temperature rise under steady state.

[0049] In step S6, all core solutions that pass performance verification will enter the multi-objective optimization and ranking stage. The software module will extract quantitative values ​​such as volume, total loss (correlated efficiency), and estimated cost based on the core database for each solution. Based on preset optimization objective weights (e.g., prioritizing volume minimization or cost minimization), these indicators will be weighted and normalized to calculate a comprehensive score for each solution. The solutions will then be ranked in descending order, generating an optimal core recommendation list and a corresponding optimal transformer recommendation list, which will be output to the user.

[0050] The magnetic core database constructed in this application not only contains the specifications of individual magnetic cores, but also expands to include performance statistics from different batches of magnetic cores. Through this pre-set database, the system can directly filter out transformer models that not only meet individual standards but also exhibit stable performance across multiple batches, precisely matching the stringent consistency requirements of semiconductor equipment. Traditional manual screening, relying solely on typical values ​​from manuals, cannot quantify and assess the performance fluctuation range of different batches, making it difficult to guarantee batch-to-batch consistency.

[0051] Please see Figure 2 In some embodiments, the set of key parameters in the area product method calculation formula is determined based on power circuit specification information, including: S21: Determine waveform factors based on topology type. ; S22: Based on the topology type and temperature rise limit ΔT, a preset parameter relationship model is invoked to calculate the window utilization coefficient that matches the power supply circuit specifications. Current density coefficient and core loss index ; S23: Match flux ripple coefficients from a pre-defined rule base based on topology type. The range of values ​​for ; S24: Based on the topology type and preset core material grade, query the core database for preset core materials at the operating frequency. Based on the recommended saturation flux density and loss curve, and the requirement to ensure loss and saturation margin, the working flux density is determined. The safe value to be determined; S25: Based on the efficiency target η and the temperature rise limit ΔT, and combined with the pre-set loss curve of the magnetic core material, a set of working magnetic flux densities that satisfy the loss and thermal constraints is determined through iterative optimization. and flux ripple coefficient ; S26: Based on the current waveform and winding configuration under the topology type, the winding structure coefficient ξ, which reflects the total copper loss relationship, is calculated.

[0052] Specifically, the pre-configured parameter relationship model is a trained machine learning regression model (e.g., deep neural network, gradient boosting decision tree, random forest regression). This model is built upon a library of historically selected successful design cases containing a large number of transformer cores. Each case in this library contains complete power circuit performance information (such as topology type, power rating, operating frequency, efficiency target, temperature rise limit, etc.) and its corresponding parameters that have been verified as optimal or near-optimal in practice. , , The software module first performs one-hot encoding on the topology type during the model training phase, and then combines it with numerical features (such as ΔT, Po, etc.). (etc.) together constitute the feature vector, and the label value is the corresponding , , Supervised learning is performed using algorithms such as gradient boosting decision trees or deep neural networks, with the goal of minimizing the error between the predicted parameters and the actual optimal parameters, ultimately resulting in a black-box mapping function that can capture complex nonlinear relationships.

[0053] In practice, when new power circuit specifications are input into the software module, the module will combine the topology type and ΔT with other relevant characteristics (such as Po, ...). (etc.) are converted together into the feature vector format required by the model; then, this feature vector is input into a pre-trained parametric relational model, which outputs a set of predicted features through internal multi-layer nonlinear calculations. , , Values; This set of predicted values ​​represents the parameter configuration that the model believes is most likely to achieve good overall performance (especially satisfying the ΔT constraint) under the current design conditions, based on historical data patterns.

[0054] The pre-built rule base stores design rules based on electromagnetic principles and engineering practices, which include a series of rules that map topology types to magnetic flux ripple coefficients. Rules for the initial value range; for example, for a single-ended flyback topology, define... The value range is from 0.4 to 0.8; for two-terminal forward or bridge topologies, it is defined as follows: The value ranges from 0.8 to 1.2. During execution, the method automatically matches and invokes the corresponding rules based on the user-selected topology type, thereby obtaining... A reasonable initial search range provides a constraint basis for subsequent optimization calculations.

[0055] For working magnetic flux density The software module is based on preset core material grades (e.g., PC95, N87, etc.) and operating frequencies. The technical data for this specific material is retrieved from the magnetic core database. The core information obtained includes the material's frequency performance. The software module retrieves a typical loss density curve (usually presented as a graph or fitting parameter) and its nominal saturation flux density Bsat. Based on the requirement of ensuring the core operates in the unsaturated region and controlling losses at a reasonable level, the software module derives an initial, conservative, safe operating flux density from the retrieved data. _safe; for example, this value can be ensured at the operating frequency by taking a certain proportion of the saturation magnetic flux density Bsat (such as 0.5 to 0.7 times) and referring to the loss curve. The corresponding loss density is determined comprehensively based on a certain preset threshold. The _safe value is set It provides an upper limit safety boundary and also offers an initial reference point from the perspective of loss.

[0056] Subsequently, to achieve better performance, the software module initiates a co-optimization phase. This phase uses the stringent performance indicators efficiency target η and temperature rise limit ΔT as core constraints, and takes the loss characteristic curve of the material obtained from the query as a key input to find a specific pair of... and The value makes the total estimated loss (including the resulting loss) in electrical calculations so that... and The calculated efficiency (including core losses and associated winding losses) is no less than η, and the hot spot temperature rise calculated based on this total loss does not exceed ΔT.

[0057] The aforementioned collaborative optimization phase is typically implemented using iterative algorithms: Within the safety limit of _safe and Within the scope of feasible experience, systematically try different ( , The system combines the following combinations: For each set of trial values, it calls the loss calculation model and the thermal network model for rapid performance evaluation; until one or more combinations that can simultaneously satisfy the hard constraints of η and ΔT are found; these combinations are the initial feasible solution set that satisfies the loss and thermal constraints, and the combination that minimizes the core volume or total loss is usually selected as the recommended initial operating point.

[0058] In some embodiments, the magnetic core determination method of this application further includes: if all candidate magnetic cores fail performance simulation verification, or the recommended results do not meet the constraints, then adjusting the key parameter set and returning the step of calculating the area product value AP based on the determined key parameter set.

[0059] Specifically, the magnetic core determination method of this application also includes an intelligent iterative optimization mechanism; that is, if all candidate magnetic cores selected based on the current set of key parameters fail to pass performance simulation verification, or if the top-ranked solutions still do not meet the constraints (such as size limits), the software module will not terminate, but will automatically analyze the reasons and intelligently adjust the determination strategy of the set of key parameters (for example, appropriately relaxing the current density to reduce temperature rise, or re-balancing the magnetic flux density to reduce volume), and then re-trigger the entire process starting from calculating the AP value until a solution that meets all conditions is found or the maximum number of iterations is reached; this forms a complete "design-verification-optimization" closed loop, which greatly improves the design success rate and efficiency.

[0060] In the above performance simulation verification steps, the core loss calculation is based on the generalized Steinmetz equation, the winding loss calculation is based on the conductor DC resistance formula or the skin and proximity effect model at high frequencies (such as the Dowell model), and the hot spot temperature rise calculation is based on the lumped parameter thermal network model. These calculation models are well known to those skilled in the art. The key to this invention is that it automatically provides the parameters required by the model through a pre-built standardized database, automatically determines the operating point of the model through the aforementioned dynamic decision-making process, and automatically executes the selection, calling, and series calculation of the model through the program, thereby realizing fast, batch, and accurate performance simulation verification of the entire candidate core list. The verification results directly serve the sorting and iterative optimization steps.

[0061] Please see Figure 3 In some embodiments, performance simulation verification based on electromagnetic fields and thermal paths includes: S51: Based on the effective cross-sectional area of ​​the candidate magnetic core and window area Based on the power circuit specifications, the number of winding turns, wire diameter, and winding structure are calculated. S52: Calculate winding losses based on the calculated number of winding turns, wire diameter, and winding structure; S53: Calculate the core loss based on the core loss parameters obtained from the core database; S54: The sum of winding loss and core loss is taken as the total core loss. The total core loss is taken as the heat source. Combined with the thermal resistance of the core and the preset heat dissipation conditions, a thermal network model is established to obtain the steady-state hot spot temperature rise.

[0062] Specifically, firstly, based on the specific effective cross-sectional area of ​​the current candidate magnetic cores... and window area In conjunction with the topology type in the initial power circuit specifications, the winding design calculation is completed. This winding design calculation can determine the number of turns on the primary and secondary sides, the appropriate nominal wire diameter, and plan the number of winding layers and the arrangement structure according to insulation and process rules, thus forming a specific winding design scheme.

[0063] Subsequently, based on this winding design, a refined calculation of losses is performed. For winding losses, the software module determines whether high-frequency effects need to be considered based on the operating frequency and wire size: if it is determined to be necessary, it calls the skin and proximity effect model (e.g., the Dowell model) to calculate AC resistance and losses; if it is determined not to be necessary, it calculates DC resistance and losses based on the resistivity, length, and cross-sectional area of ​​the wire material. For core losses, the software module obtains the loss characteristic parameters of this type of core from the core database and calls a well-known core loss calculation model (e.g., the generalized Steinmetz equation) to dynamically determine the operating magnetic flux density. and operating frequency Substitute the values ​​into the equation to calculate the core loss.

[0064] Next, the calculated winding loss is summed with the core loss to obtain the total loss of the core under the current operating conditions.

[0065] Finally, this total loss value is used as the steady-state heat source power. Combined with the thermal resistance parameters of the magnetic core obtained from the same database and the preset heat dissipation boundary conditions (such as the convective heat transfer coefficient), a simplified lumped parameter thermal network model is established. By solving the steady-state equation of the thermal network, the steady-state hot spot temperature rise of the magnetic core in the expected working environment can be calculated, thereby completing the key performance verification of whether the candidate model can meet the temperature rise limit ΔT.

[0066] In this embodiment, the lumped parameter thermal network model uses thermal-electric analogy to correspond thermal resistance, heat source, and temperature to resistance, current source, and voltage in a circuit, respectively, thereby establishing a simplified thermal circuit model for calculation.

[0067] In this invention, the specific application is as follows: the program sets the total loss value calculated in the aforementioned steps as a constant heat source in the thermal network; retrieves and loads the nominal thermal resistance parameters of the current candidate magnetic core model from the magnetic core database; and, in conjunction with preset typical heat dissipation boundary conditions (such as additional thermal resistance representing the influence of the heat sink or chassis), constructs a simplified thermal network suitable for the magnetic core model; and then, by solving the steady-state temperature field of the network, the estimated temperature rise of the magnetic core hot spot can be obtained.

[0068] In some embodiments, winding losses are calculated based on the calculated number of winding turns, wire diameter, and winding structure; further, if the operating frequency... The frequency exceeds a preset threshold, and / or the wire diameter estimated based on the calculated wire diameter is greater than or equal to the operating frequency. If the skin depth δ of the conductor is a preset multiple, the winding loss is calculated using the skin effect and proximity effect models; otherwise, the winding loss is calculated based on the DC resistance and effective current value of the winding structure.

[0069] Specifically, when calculating winding losses, it is necessary to determine whether high-frequency effects need to be taken into account. The specific criterion for this determination is: if the operating frequency... If the frequency exceeds a preset critical frequency threshold (e.g., 50kHz or 100kHz), regardless of the wire thickness, it is determined that the high-frequency loss model must be enabled to ensure the rigor of calculations in all high-frequency applications.

[0070] If the operating frequency If the critical frequency threshold is not exceeded, a more refined wire size determination is performed: the wire diameter is estimated based on the wire diameter calculated in the aforementioned embodiments, and its operating frequency is calculated based on the estimated wire diameter. The skin depth δ is calculated as follows: if the estimated conductor diameter is greater than or equal to k times the skin depth δ (k is a preset engineering constant, such as 1.0 or 1.5), then the skin effect is also determined to be non-negligible.

[0071] Once any of the above conditions are met, the system will call an engineering model (such as the Dowell model) that covers skin and proximity effects to perform accurate calculations. This model takes the specific number of layers, number of turns per layer, wire diameter, and insulation structure of the winding as inputs to calculate the equivalent AC resistance and loss of the winding.

[0072] Conversely, if the operating frequency If the threshold is not exceeded and the conductor diameter is less than k times the skin depth, the system determines that the high-frequency effect can be ignored and adopts a simplified path: directly calculate the DC resistance of the winding conductor based on the total length, cross-sectional area and material resistivity, and then combine the effective value of the current of each winding determined by the topology type to calculate the winding loss according to Joule's law.

[0073] In some embodiments, the formula for calculating the area product value AP includes: ; Where η is the efficiency target in the indicator information, Po is the rated output power in the indicator information, and ξ is the winding structure coefficient in the set of key parameters. This refers to the core loss index within the set of key parameters. The waveform factor in the set of key parameters, D, is the duty cycle determined based on the topology type and the input-output voltage relationship. The window utilization coefficient is the key parameter set. The current density coefficient is the value in the set of key parameters. The operating magnetic flux density is defined in the set of key parameters. The flux ripple coefficient is the value in the set of key parameters. The operating frequency is the value in the aforementioned indicator information.

[0074] In this embodiment, the core formula used to calculate the area product value AP is an engineering expression derived based on Faraday's law of electromagnetic induction and Ampere's law, taking into account the electrical performance, thermal constraints, and engineering feasibility of high-frequency transformers in actual operation. This formula not only includes the basic electrical quantities that determine the change of magnetic flux (such as power, frequency, and voltage waveform), but also, more importantly, embeds performance parameters that reflect the design goals and limitations (such as efficiency and temperature rise). This transforms it from a theoretical geometric correlation formula into an evaluation tool with clear constraints that can directly respond to multi-dimensional power circuit index information.

[0075] The specific form of the formula is as before, and the parameters are determined collaboratively as follows: waveform factor Window utilization coefficient Current density coefficient Working magnetic flux density Magnetic flux ripple coefficient Core loss index The winding structure coefficient ξ has been determined in the dynamic collaborative decision-making process of the previous embodiment. These parameters are not isolated fixed values, but are optimized combinations generated collaboratively by parameter relationship models, rule base queries and optimization algorithms, under the core constraints such as efficiency target η and temperature rise limit ΔT.

[0076] The duty cycle D in the formula is directly calculated from the input-output voltage relationship input by the user and the basic circuit equations of the selected topology type, the rated output power Po, and the operating frequency. The target efficiency η itself comes from the power circuit specification information provided by the user or other means (such as reading and calculating based on design drawings using electronic design software, etc.). The determination process of this series of parameters together ensures that the final calculated AP value accurately reflects the minimum core geometry capacity required to transmit a specified power under specific efficiency and temperature rise requirements.

[0077] The reason this application adopts this comprehensive formula, rather than the generalized AP formula which only includes basic parameters such as power and frequency, is that the determination method aims to solve optimization problems under multiple constraints. Traditional generalized AP formulas usually only relate to power and core size, while this formula introduces efficiency η and temperature rise-related parameters (…). , The calculation incorporates key design constraints such as efficiency targets, thermal limitations, and specific topology current waveforms directly into the calculation, including the winding structure coefficient ξ. This transforms the calculated AP value from a broad preliminary reference requiring extensive manual verification into a precise "demand specification" with pre-defined thermal and electrical performance boundary conditions. This formula forms the technical foundation for the leap from "rough estimation" to "precise demand definition," enabling subsequent database selection to focus directly on core models that truly have the potential to meet all design objectives, thereby significantly improving the efficiency and accuracy of the entire screening process.

[0078] Please see Figure 4 In some embodiments, the candidate magnetic cores that have passed the verification are ranked according to a preset optimization objective, including: S61: Obtain the volume, total loss, and cost quantifications for each validated candidate core solution; S62: Based on the preset weighting coefficients, perform weighted normalization on the quantified values ​​of volume, total loss and cost, and calculate the comprehensive score of each scheme. S63: Based on the comprehensive score, all schemes are sorted in descending order to generate a recommended list of candidate magnetic cores that have passed the verification and a list of related candidate transformers.

[0079] Specifically, the preset optimization goals are reflected in the comprehensive evaluation indicators of the design scheme in three dimensions: geometric dimensions, electrical performance, and economy. These indicators are the physical volume of the magnetic core, the total loss during operation, and the estimated cost based on market data. Users or software modules can set clear priorities for these three competitive indicators. For example, in the "compact design" scenario that pursues extreme compactness, the software module can preset the weight coefficient of volume to 0.5, the weight of total loss to 0.3, and the weight of cost to 0.2. In the "high-efficiency application" that pursues high efficiency and energy saving, the weight of total loss is preset to be the highest, such as 0.6, the weight of volume to 0.3, and the weight of cost to 0.1. Users can also directly input a custom weight vector through the interactive interface, such as [0.4, 0.4, 0.2], to express their specific need for equal emphasis on volume and loss.

[0080] The specific operation of weighted normalization is as follows: The software module first normalizes the volume, total loss, and cost values ​​of all verified schemes, that is, the best value in each indicator (such as minimum volume, minimum loss, and minimum cost) is mapped to 1, the worst value is mapped to 0, and the intermediate values ​​are linearly interpolated to obtain a score between 0 and 1; then, the normalized scores of each scheme on the three indicators are multiplied by the aforementioned preset weight coefficients, and then summed to obtain the comprehensive score of the scheme; for example, if a scheme has normalized scores of 0.8, 0.9, and 0.5 in volume, loss, and cost, respectively, and a weight of [0.5, 0.3, 0.2] is used, its comprehensive score is 0.8*0.5 + 0.9*0.3 + 0.5*0.2 = 0.77. Finally, the software module sorts all the solutions in descending order based on this comprehensive score, and the one with the highest score is the optimal recommended solution under the current optimization goal, thereby generating and outputting a quantitative list of candidate magnetic cores that reflects the user's specific value orientation.

[0081] Subsequently, the software module can further generate a list of candidate transformers based on the generated candidate magnetic core list and the relevant transformer selection standards (national level) for users to choose from.

[0082] It should be noted that in the above embodiments, there is no necessary order between the steps. Those skilled in the art can understand from the description of the embodiments of this application that the steps may have different execution orders in different embodiments, that is, they may be executed in parallel or in turn, etc.

[0083] Furthermore, this application provides a method for screening power transformers for semiconductor devices. The selected power transformers are high-performance power components specifically designed for various core equipment in semiconductor manufacturing processes. Their application scenarios have highly specialized and extremely stringent technical requirements; these application scenarios include: 1. Power supply system for etching equipment In plasma etching machines, transformers must provide stable power transmission to the RF power generator while withstanding severe electrical stress under high-frequency, high-power-density conditions. These transformers place extremely high demands on the magnetic core's permeability stability, loss characteristics, and thermal management capabilities; any parameter drift will directly affect the uniformity and precision of the etching process.

[0084] 2. High-voltage power supply for ion implanter Ion implantation equipment requires high-voltage DC power supplies ranging from tens to hundreds of kilovolts. Its transformers operate in a high-frequency, high-voltage environment, placing stringent requirements on insulation performance, partial discharge suppression capabilities, and the magnetic saturation characteristics of the core. Improper core selection can directly lead to high-voltage breakdown or inaccurate implantation dosage, resulting in the scrapping of large quantities of wafers.

[0085] 3. Heating power supply for thin film deposition equipment In physical vapor deposition (PVD) or chemical vapor deposition (CVD) equipment, transformers provide precisely controlled AC power to the heaters. These applications require transformers to maintain high efficiency and low temperature rise over a wide load range to ensure uniformity of the temperature field within the reaction chamber and guarantee the quality of the deposited thin film.

[0086] 4. Precision power supply for wafer inspection equipment In precision testing equipment such as automated test probe stations, transformers must provide clean, low-ripple power to high-precision measurement circuits, while also meeting stringent requirements for electromagnetic interference suppression. The material properties of the magnetic core and the selection of the operating point directly affect the signal noise level and testing accuracy.

[0087] 5. Power supply for equipment auxiliary systems Various pumps, valves, sensors, and control systems inside semiconductor equipment require multiple isolated power supplies. Transformers need to achieve multi-winding output within a compact space, while also having high requirements for cross-regulation and temperature rise uniformity under different load conditions.

[0088] Furthermore, corresponding to the different specific application scenarios, different user types, and different integration requirements mentioned above, the transformer screening method for semiconductor device power supplies in this application embodiment can also be deployed in different ways; for example, please refer to Figure 5When the user is an independent power supply design engineer, a small design studio, or when the selected transformer is used in etching equipment with a high degree of confidentiality, the method of this application can be deployed locally, that is, the desktop application of this method can be installed directly on the user's workstation (or work computer). The program contains a complete parameter decision engine, core database and simulation verification module.

[0089] Please see Figure 6 When the target user is a medium-sized power supply design team (with different members responsible for power supply development for different models of semiconductor equipment), the method of this application can be deployed in a client / server manner. Specifically, the IT department deploys a high-performance server in the server room, installs the server-side software of this method, and configures an enterprise-level magnetic core database and transformer database. Each team member's workstation installs a lightweight client and connects to the server via the enterprise intranet. When designing a new high-voltage power supply, the client sends the requirements to the server, which calls the algorithm to complete the calculation and returns the result. Simultaneously, the design data is automatically stored in the server's historical case library. Furthermore, if other team members design power supplies of similar specifications, they can directly refer to the successful cases of the aforementioned team members and call similar parameters.

[0090] Please see Figure 7 When the user is a large semiconductor equipment supplier or an international semiconductor equipment supplier (with R&D centers in multiple regions), the method of this application can also be deployed in the cloud. For example, an international semiconductor equipment supplier has three R&D centers in locations A, B, and C, jointly developing a new generation of PVD / CVD equipment power systems. The company subscribes to the cloud service of this method, and engineers in the three locations access the same platform through a browser. After the team in location B completes the selection of the magnetic core and transformer for a certain model of PVD equipment power supply, the data is automatically synchronized to the cloud. When designing similar equipment, the team in location A can refer to the successful case of the team in location B and call similar parameters.

[0091] Please see Figure 8When the user has a highly automated product development platform, the method of this application can also be deployed using an embedded integration approach. For example, a wafer inspection equipment manufacturer has established a highly automated product development platform. Power system design, as one part of this platform, needs to be seamlessly integrated with the overall mechanical design, electrical design, and thermal simulation systems. The enterprise's IT team integrates the API (Application Programming Interface) of this method into its PLM (Product Lifecycle Management) system. When the overall system designer defines new product specifications in the PLM, the system automatically generates power specifications and calls this method via the API, passing in parameters such as power, frequency, efficiency, and temperature rise. The API service completes the calculation in the cloud (or on the enterprise's internal server) and returns the selected core model. The PLM system automatically associates this model with the BOM (Bill of Materials) and triggers subsequent winding design and thermal simulation processes. The entire process requires no manual intervention, achieving fully automated design from overall specifications to power components.

[0092] Furthermore, when addressing the issue of "high batch consistency," the method described in this application can also be integrated with ERP / MES systems (SAP Enterprise Resource Planning / Manufacturing Execution System). For example, a semiconductor equipment manufacturer produces dozens of etching machines per month, requiring a power transformer for each machine. Due to the different processes used by different customers, the transformer specifications vary slightly, but all must maintain extremely high batch consistency. The company deploys this method in its SAP ERP system and integrates it with the MES system. When the production planning system generates an equipment order, it automatically extracts the required power specifications for the equipment, filters out the core and transformer models that meet the requirements through the module of this method in the ERP system, and further queries the batch information of that transformer model in the inventory module, compares the batch performance statistics, and selects the inventory batch with the closest performance to historical batches. If the inventory is insufficient, the system automatically generates a purchase order with explicit performance consistency requirements (e.g., "performance parameters must deviate from those of our company's batch 20250301 by <2%"). After the transformers arrive, the quality inspection department conducts incoming material inspection according to the inspection standards generated by the system, and the test data is automatically entered into the batch performance database. After passing inspection, the batch of magnetic cores was designated for use in the production of specific equipment to ensure that the performance of the new batch was highly consistent with that of the previous batches.

[0093] In addition, those skilled in the art can refer to the prior art to learn the specific technical means of how to further determine the transformer model based on the candidate magnetic core model, which the inventors will not elaborate on in this embodiment.

[0094] One embodiment of this application provides a storage medium storing a computer program configured as a transformer screening method for semiconductor device power supplies in Embodiment 1.

[0095] The storage medium in this application refers to a non-transitory computer-readable storage medium containing computer programs, such as storage space in a USB flash drive, solid-state drive, optical disk, or network server.

[0096] The storage medium contains a complete computer program, in binary code, for executing the aforementioned transformer selection method for semiconductor device power supplies. When the storage medium is connected to a computing device (such as a personal computer, workstation, or server), the program stored within it can be read by the device's processor and loaded into memory for execution. This program contains a set of logical instructions for implementing all steps of the aforementioned transformer selection method for semiconductor device power supplies, specifically including: input module code for receiving and parsing user power circuit indicator information; decision engine code for dynamically and collaboratively determining key parameter sets, which encapsulates a pre-trained parameter relationship model and rule base; algorithm code for calculating the area product value AP; interface code for connecting to and querying a standardized magnetic core database; numerical calculation code for performing electromagnetic field and thermal circuit performance simulation verification; and processing code for multi-objective optimization ranking and result output.

[0097] By running this program, the entire process of determining the optimal recommendation list, from inputting requirements to obtaining the finalized list, can be fully implemented on the computing device, thereby transforming the method into a repeatable and deployable technical product.

[0098] Please see Figure 9 An electronic device according to an embodiment of this application includes a memory, a processor, and a computer program stored in the memory. The program is connected to the processor via a bus. When the processor executes the computer program, it implements the transformer screening method for semiconductor device power supply in Embodiment 1.

[0099] Specifically, the processor is configured to support the electronic device in performing the corresponding functions in the methods described in the above method embodiments. The processor may be a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof. The aforementioned hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The aforementioned PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0100] Memory is used to store program code, etc. Memory can include volatile memory (VM), such as random access memory (RAM); memory can also include non-volatile memory (NVM), such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); memory can also include combinations of the above types of memory.

[0101] The memory can be used to store non-volatile software programs, non-volatile computer executable programs, and modules, such as the transformer screening method for semiconductor device power supplies in the embodiments of this application.

[0102] The memory may include a program storage area and a data storage area, wherein the program storage area may store the operating system and an application program required for at least one function.

[0103] In some embodiments, the memory may include memory remotely configured relative to the processor, and these remote memories may be connected via a network to means of implementing a transformer screening method for a semiconductor device power supply. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0104] The electronic devices in this application embodiment may specifically be ultra-mobile personal computer devices, smart displays or all-in-one machines, servers or server clusters, etc.

[0105] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A method of screening transformers for semiconductor device power supplies, characterized by, include: Receive power supply circuit specification information; the power supply circuit specification information includes at least: rated output power Po, topology type, and operating frequency. Efficiency target η and temperature rise limit ΔT; Based on the power circuit index information, a set of key parameters is determined for the transformer area product method calculation formula; the set of key parameters includes at least: waveform factor. Window utilization coefficient Current density coefficient Working magnetic flux density Magnetic flux ripple coefficient Core loss index , winding structure coefficient ξ; Calculate the area product value AP based on the aforementioned key parameter set; From the pre-built magnetic core database, retrieve and filter all candidate magnetic core models that are greater than or equal to the calculated area product value AP, and generate a candidate list; For each magnetic core model in the candidate list, a performance simulation verification based on electromagnetic field and thermal path is performed. The performance includes at least: winding loss, magnetic core loss, total loss and hot spot temperature rise. Based on the preset optimization target, the verified candidate magnetic cores are sorted, and the list of verified candidate magnetic cores and the related candidate transformer list are output. The step of determining the key parameter set in the transformer area product method calculation formula based on the power circuit index information includes: determining the waveform factor based on the topology type. Based on the topology type and the temperature rise limit ΔT, a preset parameter relationship model is invoked to calculate the window utilization coefficient that matches the power supply circuit specifications. Current density coefficient and core loss index Based on the topology type, the flux ripple coefficient is matched from a preset rule base. The value range; based on the topology type and the preset core material grade, query the core database for the preset core material at the operating frequency. Based on the recommended saturation flux density and loss curve, and the requirement to ensure loss and saturation margin, the working flux density is determined. The safe value is determined; based on the efficiency target η and the temperature rise limit ΔT, and combined with the preset loss curve of the magnetic core material, a set of working magnetic flux densities that satisfy the loss and thermal constraints is determined through iterative optimization. and flux ripple coefficient Based on the aforementioned topology, the winding structure coefficient ξ, which reflects the total copper loss relationship, is calculated.

2. The method according to claim 1, characterized in that, The magnetic core database is configured as follows: A standardized database containing digital records of various types of magnetic cores; each core record includes at least: effective cross-sectional area. Window area Area product value AP, saturation magnetic flux density, loss parameters, and thermal resistance.

3. The method according to claim 2, characterized in that, The performance simulation verification based on electromagnetic fields and thermal circuits includes: According to the candidate magnetic core and Based on the power circuit specifications, the number of winding turns, wire diameter, and winding structure are calculated. Based on the calculated number of turns, wire diameter, and winding structure, the winding loss is calculated. The core loss is calculated based on the core loss parameters obtained from the core database. The sum of the winding loss and the core loss is taken as the total core loss. The total core loss is taken as the heat source. A thermal network model is established by combining the thermal resistance of the core and the preset heat dissipation conditions to obtain the steady-state hot spot temperature rise.

4. The method according to claim 3, characterized in that, The calculation of winding losses based on the calculated number of turns, wire diameter, and winding structure includes: If the operating frequency The frequency exceeds a preset threshold, and / or the wire diameter estimated based on the calculated wire diameter is greater than or equal to the operating frequency. If the skin depth δ of the conductor is a preset multiple, the winding loss is calculated using the skin effect and proximity effect models; otherwise, the winding loss is calculated based on the DC resistance and effective current value of the winding structure.

5. The method according to claim 1, characterized in that, The formula for calculating the area product value AP includes: ； Where η is the efficiency target in the power supply circuit specifications, Po is the rated output power in the power supply circuit specifications, and ξ is the winding structure coefficient in the set of key parameters. This refers to the core loss index within the set of key parameters. The waveform factor in the set of key parameters, D, is the duty cycle determined based on the topology type and the input-output voltage relationship. The window utilization coefficient is the key parameter set. The current density coefficient is the value in the set of key parameters. The operating magnetic flux density is defined in the set of key parameters. The flux ripple coefficient is the value in the set of key parameters. The operating frequency is the parameter information of the power supply circuit.

6. The method according to claim 1, characterized in that, The process of ranking the verified candidate magnetic cores according to a preset optimization objective includes: Obtain the volume, total loss, and cost quantification values ​​for each validated candidate magnetic core solution; Based on preset weighting coefficients, the quantified values ​​of volume, total loss, and cost are weighted and normalized to calculate the comprehensive score of each scheme. Based on the comprehensive score, all schemes are sorted in descending order to generate a recommended list of verified candidate magnetic cores and a related candidate transformer list.

7. A storage medium storing a computer program, characterized in that, The computer program is configured to perform the method according to any one of claims 1 to 6.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 6.

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