Optimization Method and System for Internal Electric Field Distribution of Silicon Carbide High-Voltage MOSFET

Abstract

The present invention discloses an internal electric field distribution optimization method and system for a silicon carbide high-voltage MOSFET, which relates to the field of semiconductor technology. The method includes: establishing a three-dimensional model of the silicon carbide high-voltage MOSFET to obtain a MOSFET device model; constructing a doping optimization module; performing simulations of the electric field distribution and the thermal field distribution to obtain the electric field concentration region and the thermal field concentration region in the drift region; introducing gradient doping to output a first doping optimization distribution; introducing gradient doping to output a second doping optimization distribution; and performing collaborative optimization in the drift region. The present invention solves the technical problem in the prior art that the electric field and thermal field distributions in the drift region of the silicon carbide high-voltage MOSFET technology are poor, resulting in insufficient device performance, stability and reliability, and achieves the technical effect of optimizing the electric field and thermal field distributions in the drift region of the silicon carbide high-voltage MOSFET, and improving the device performance, stability and reliability.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor technology, and particularly to an internal electric field distribution optimization method and system for a silicon carbide high-voltage MOSFET. Background Art

[0002] In the current field of semiconductor technology, silicon carbide (SiC) high-voltage metal-oxide-semiconductor field-effect transistors (MOSFETs), as an important power device, are widely used in power electronic systems, such as new energy vehicles, high-voltage direct current power transmission, and industrial motor drives. However, with the continuous improvement of the performance requirements for power devices in these applications, the existing silicon carbide high-voltage MOSFET technology has gradually exposed some limitations. In the traditional device design and manufacturing process, the electric field and thermal field distributions in the drift region are often difficult to reach the ideal state. Due to the existence of the electric field concentration region, the local electric field intensity may be too high, which will not only increase the risk of electrical breakdown of the device, reduce its breakdown voltage, but also affect the carrier transport characteristics, thereby increasing the on-resistance, rising the power consumption of the device, and reducing the energy conversion efficiency. At the same time, the emergence of the thermal field concentration region will cause the problem of too high local temperature. The too high temperature will accelerate the aging and performance degradation of the material, such as causing the mobility of the semiconductor material to decrease, the threshold voltage to drift, etc., seriously affecting the long-term stability and reliability of the device. Moreover, the uneven thermal field distribution will also generate thermal stress, which may cause damage to the device structure and further shorten the service life of the device.

[0003] There are technical problems in the existing silicon carbide high-voltage MOSFET technology that the electric field and thermal field distributions in the drift region are poor, resulting in insufficient device performance, stability, and reliability. Summary of the Invention

[0004] The present application provides an internal electric field distribution optimization method and system for a silicon carbide high-voltage MOSFET, which are used to solve the technical problems in the existing silicon carbide high-voltage MOSFET technology that the electric field and thermal field distributions in the drift region are poor, resulting in insufficient device performance, stability, and reliability.

[0005] In view of the above problems, the present application provides an internal electric field distribution optimization method and system for a silicon carbide high-voltage MOSFET.

[0006] In the first aspect of the present application, an internal electric field distribution optimization method for a silicon carbide high-voltage MOSFET is provided. The method includes:

[0007] A three-dimensional model of a silicon carbide high-voltage MOSFET is established to obtain a MOSFET device model; a doping optimization module is constructed, and an initial doping distribution is input according to the doping optimization module; based on a TCAD tool, the electric field distribution and the thermal field distribution of the MOSFET device model are simulated according to the initial doping distribution, and the electric field concentration region and the thermal field concentration region in the drift region of the MOSFET device model are obtained; the doping optimization module introduces gradient doping in the electric field concentration region and outputs a first doping optimization distribution; the doping optimization module introduces gradient doping in the thermal field concentration region and outputs a second doping optimization distribution; co-optimization is performed in the drift region according to the first doping optimization distribution and the second doping optimization distribution.

[0008] In the second aspect of the present application, an internal electric field distribution optimization system for a silicon carbide high-voltage MOSFET is provided. The system includes:

[0009] A MOSFET device model acquisition module, which is used to establish a three-dimensional model of a silicon carbide high-voltage MOSFET and obtain a MOSFET device model; an initial doping distribution input module, which is used to construct a doping optimization module and input an initial doping distribution according to the doping optimization module; a concentration region acquisition module, which is based on a TCAD tool to simulate the electric field distribution and the thermal field distribution of the MOSFET device model according to the initial doping distribution, and obtain the electric field concentration region and the thermal field concentration region in the drift region of the MOSFET device model; a first doping optimization distribution output module, which is used for the doping optimization module to introduce gradient doping in the electric field concentration region and output a first doping optimization distribution; a second doping optimization distribution output module, which is used for the doping optimization module to introduce gradient doping in the thermal field concentration region and output a second doping optimization distribution; a co-optimization module, which is used to perform co-optimization in the drift region according to the first doping optimization distribution and the second doping optimization distribution.

[0010] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0011] A three-dimensional model of a silicon carbide high-voltage MOSFET is established to obtain a MOSFET device model; a doping optimization module is constructed, and an initial doping distribution is input according to the doping optimization module; the electric field distribution and the thermal field distribution of the MOSFET device model are simulated to obtain the electric field concentration region and the thermal field concentration region in the drift region of the MOSFET device model; the doping optimization module introduces gradient doping in the electric field concentration region and outputs a first doping optimization distribution; the doping optimization module introduces gradient doping in the thermal field concentration region and outputs a second doping optimization distribution; the first doping optimization distribution and the second doping optimization distribution are used for collaborative optimization in the drift region. The technical effects of optimizing the electric field and thermal field distributions in the drift region of the silicon carbide high-voltage MOSFET, improving the device performance, stability and reliability are achieved. Description of the Drawings

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0013] Figure 1 Schematic flow chart of the method for optimizing the internal electric field distribution of the silicon carbide high-voltage MOSFET provided by the embodiment of the present application;

[0014] Figure 2 Schematic structural diagram of the system for optimizing the internal electric field distribution of the silicon carbide high-voltage MOSFET provided by the embodiment of the present application.

[0015] Description of the reference numerals: MOSFET device model acquisition module 10, initial doping distribution input module 20, concentration region acquisition module 30, first doping optimization distribution output module 40, second doping optimization distribution output module 50, collaborative optimization module 60. Detailed Embodiments

[0016] The present application provides a method and a system for optimizing the internal electric field distribution of a silicon carbide high-voltage MOSFET, which are used to solve the technical problem that the electric field and thermal field distributions in the drift region of the silicon carbide high-voltage MOSFET technology in the prior art are not good enough, resulting in insufficient device performance, stability and reliability.

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0018] Embodiment 1, as Figure 1 shown, the present application provides a method for optimizing the internal electric field distribution of a silicon carbide high-voltage MOSFET, and the method includes:

[0019] Step S100: Establish a three-dimensional model of the silicon carbide high-voltage MOSFET to obtain a MOSFET device model.

[0020] Specifically, using professional electronic design automation (EDA) software, with its powerful modeling function, the process of constructing the three-dimensional model of the silicon carbide high-voltage MOSFET is started. First, according to the unique physical properties of the silicon carbide material, such as high breakdown electric field, high electron mobility, etc., the material parameters of each layer in the model are accurately set, including key attributes such as conductivity, dielectric constant, and bandgap width, to ensure that the model can accurately reflect the electrical behavior of the actual device. Then, according to the standard structure of the MOSFET, the geometric shapes and sizes of each region such as the source, drain, gate, and drift region are carefully depicted, and the physical structure of the device is accurately restored from the microscopic level. Among them, when setting parameters such as the length, width, and thickness of the drift region, performance indicators such as breakdown voltage requirements and on-resistance need to be comprehensively considered to ensure their rationality. After these basic settings are completed, through complex mesh generation techniques, the three-dimensional model is discretized into numerous tiny units, so that the physical equations can be more accurately solved during subsequent numerical calculations and simulation analyses. After a series of rigorous operations and parameter adjustments, a highly accurate MOSFET device model that can truly reflect the physical characteristics and electrical behavior of the silicon carbide high-voltage MOSFET is finally successfully obtained, providing a solid and reliable basic platform for subsequent doping profile optimization and electric field and thermal field analyses, and ensuring that the entire optimization process can steadily advance from an accurate starting point.

[0021] Step S200: Construct a doping optimization module and input an initial doping profile according to the doping optimization module.

[0022] Specifically, an optimized doping module is carefully constructed by applying advanced programming techniques and professional circuit design software tools. This module has powerful functions and flexible parameter configuration capabilities, aiming to provide strong support for subsequent doping profile optimization. When initializing the doping profile input, the value range of the doping concentration is first clearly defined. The determination of this range needs to comprehensively consider the inherent characteristics of the silicon carbide material, the expected electrical performance of the device, as well as past experimental data and theoretical research results. Then, an exponential decay model is adopted as the doping profile formula. Among them, the maximum doping concentration is accurately set on the side close to the source in the MOSFET device model. The determination of its value has undergone in-depth analysis of the carrier injection and transport characteristics near the source to ensure that a suitable conductive channel can be formed in this area; while the parameter affecting the doping concentration decay rate is accurately assigned through the simulation and research of the drift region electric field distribution and carrier diffusion law, so as to achieve uniform doping in the drift region. Finally, an initial doping profile that meets the actual needs and has a scientific basis is successfully input, laying a key foundation for subsequent electric field and thermal field simulations and further optimization work based on this profile, guiding the entire doping optimization process to move forward in an orderly manner in the expected direction.

[0023] Step S300: Based on the TCAD tool, perform electric field distribution and thermal field distribution simulations on the MOSFET device model according to the initialized doping profile, and obtain the electric field concentration region and thermal field concentration region in the drift region of the MOSFET device model.

[0024] Specifically, give full play to the powerful simulation capabilities of the TCAD (Technology Computer-Aided Design) tool, and accurately apply the previously determined initial doping distribution to the constructed MOSFET device model. Based on the basic principles of semiconductor physics and complex numerical algorithms, the TCAD tool conducts comprehensive and in-depth simulation calculations on the transport behavior of electrons and holes, the generation and distribution of electric fields, and the current conduction mechanism in the model. In terms of electric field distribution simulation, by solving the Poisson equation and the current continuity equation, accurately depict the electric field distribution in each region of the MOSFET device, especially in the drift region, and can clearly identify the regions with high and concentrated electric field intensity. These regions are often formed due to the non-uniformity of doping concentration, the geometric shape of the device structure, or the influence of operating conditions, and may have a key impact on the breakdown voltage performance of the device. At the same time, in the thermal field distribution simulation, considering the Joule heat generated when current passes through the device and heat transfer mechanisms such as heat conduction and heat convection, combined with thermal physical parameters such as the thermal conductivity and specific heat capacity of the material, detailedly calculate the distribution state of the thermal field in the entire device, and then accurately locate the regions with concentrated thermal field. These regions may cause problems such as material performance degradation and reduced reliability due to excessive temperature. Through this series of rigorous and complex simulation processes, finally accurately obtain the regions with concentrated electric field and thermal field in the drift region of the MOSFET device model, providing key target regions for subsequent targeted doping optimization, enabling the optimization measures to directly address the core issues affecting device performance and reliability, and strongly promoting the process of optimizing the internal electric field distribution of silicon carbide high-voltage MOSFETs, ensuring that the device can operate in a more stable and efficient state.

[0025] Step S400: The doping optimization module introduces gradient doping in the electric field concentration region and outputs the first doping optimization distribution.

[0026] Specifically, with the help of the constructed doping optimization module, key optimization operations are carried out on the electric field concentration region accurately located through simulation. First, based on the in-depth understanding of the electrical characteristics of the silicon carbide high-voltage MOSFET and the support of a large amount of experimental data and theoretical models, the initial parameters of gradient doping are determined in this region, that is, the first initial doping concentration and the first gradient width are obtained. Among them, the selection of the first initial doping concentration should comprehensively consider the degree of electric field concentration, the original doping distribution, and the expected electric field homogenization effect. Through precise calculation and empirical judgment, a starting doping concentration value that can effectively adjust the electric field distribution is set; the determination of the first gradient width needs to consider factors such as the range, shape, and surrounding doping environment of the electric field concentration region to ensure that the gradient doping can gradually adjust the electric field distribution within a suitable spatial range and avoid over-doping or under-doping. Subsequently, a pre-set doping distribution formula is called. This formula is carefully designed to accurately calculate the change of doping concentration at different positions in the electric field concentration region according to the input parameters. The doping optimization module strictly performs a detailed linear gradient doping operation on the electric field concentration region according to the settings of the first initial doping concentration and the first gradient width. During this process, the electric field distribution after doping is continuously monitored and analyzed in real time. Through a complex electric field calculation model, the change of the electric field intensity in the electric field concentration region and the uniformity index of its distribution are accurately identified. If it is detected that the uniformity of the electric field intensity distribution in the electric field concentration region is less than the preset uniformity at this time, it indicates that the current doping effect has not reached the ideal state and further optimization is required. Therefore, with the preset uniformity as the clear optimization goal, the iterative simulation process is started. In each iteration, the doping optimization module will intelligently fine-tune parameters such as the first initial doping concentration and the first gradient width according to the results of the previous simulation, and then perform doping and electric field distribution calculations again. This process is repeated continuously to gradually approach the requirements of the preset uniformity. After multiple rigorous iterative simulations, the first optimized initial doping concentration and the first optimized gradient width that can make the uniformity of the electric field intensity distribution in the electric field concentration region meet the preset requirements are finally determined, and based on these optimized parameters, the first doping optimization distribution is output. This distribution will be an important part of the subsequent co-optimization steps, laying a solid foundation for comprehensively improving the electric field distribution uniformity and overall performance of the silicon carbide high-voltage MOSFET, and strongly promoting the entire device optimization process to develop in a more stable and efficient direction.

[0027] Step S500: The doping optimization module introduces gradient doping in the thermal field concentration region and outputs the second doping optimization distribution.

[0028] Specifically, the existing doping optimization module is used to focus on the thermal field concentration area to implement the optimization strategy. First, based on a profound understanding of the thermal properties of silicon carbide materials and the heat conduction mechanism of MOSFET devices, combined with the previous simulation data and the thermal field optimization experience of similar devices, the second initial doping concentration and the second gradient width applicable to the thermal field concentration area are determined. The selection of the second initial doping concentration needs to consider the thermal field distribution, the thermal stability of the material, and the desired thermal equilibrium effect. After rigorous theoretical calculations and practical verifications, an initial doping concentration value that helps improve the thermal field is set; the determination of the second gradient width is based on the geometric characteristics, heat flow direction, and thermal coupling relationship with the surrounding area of the thermal field concentration area, ensuring that the gradient doping can accurately act on the thermal field concentration area, achieve effective thermal field control, and avoid new thermal problems caused by improper doping. Then, the established doping distribution formula, which is optimized and adjusted according to the characteristics of the thermal field concentration area, is called to accurately calculate the doping concentration changes at various locations within the thermal field concentration area based on the input parameters, thereby guiding the doping optimization module to perform a fine linear gradient doping operation on the thermal field concentration area according to the second initial doping concentration and the second gradient width. During this process, an advanced thermal field analysis model is continuously used to monitor and quantitatively evaluate the temperature rise situation in the thermal field concentration area in real time, accurately identifying the temperature rise indicators in the thermal field concentration area, including key parameters such as the temperature peak, average temperature rise rate, and uniformity of temperature distribution. If it is detected that the temperature rise indicators in the thermal field concentration area are less than the preset temperature rise indicators, it means that the current thermal field optimization degree does not meet the expectations, and then the iterative simulation process is started with the preset temperature rise indicators as the clear optimization goal. In each iteration, the doping optimization module cleverly adjusts key parameters such as the second initial doping concentration and the second gradient width according to the results of the previous round of simulation, performs doping and thermal field calculations again, and evaluates the new temperature rise indicators. By repeating this cycle, through multiple rigorous iterative simulations, the second optimized initial doping concentration and the second optimized gradient width that can make the temperature rise indicators in the thermal field concentration area meet the preset requirements are finally determined, and then the second doping optimization distribution is output based on these optimized parameters. This distribution will participate in the subsequent collaborative optimization link together with the first doping optimization distribution, providing strong support for effectively solving the thermal field concentration problem of MOSFET devices, improving the thermal stability and overall reliability of the devices, further promoting the optimization process of the internal electric field and thermal field distribution of silicon carbide high-voltage MOSFETs, ensuring that the devices can operate stably and reliably under complex working conditions, extending their service life and improving their working performance.

[0029] Step S600: Perform collaborative optimization in the drift region according to the first doping optimization distribution and the second doping optimization distribution.

[0030] Specifically, based on the obtained first optimized doping distribution and second optimized doping distribution, a comprehensive and in-depth co-optimization process is carried out in the drift region of the MOSFET device. First, the close coupling relationship between the electric field and the thermal field, as well as the mutual influence of their distributions in the drift region, is fully considered. The first optimized doping distribution aims to improve the uniformity of the electric field distribution in the electric field concentration region, while the second optimized doping distribution focuses on alleviating the temperature rise problem in the thermal field concentration region. However, these two are not isolated but interrelated. During co-optimization, through complex numerical algorithms and physical models, the overall doping distribution in the drift region is comprehensively adjusted. According to the electric field regulation region and the doping concentration change law determined by the first optimized doping distribution, combined with the thermal field optimization region targeted by the second optimized doping distribution and the corresponding doping scheme, the doping concentration at each position in the drift region is accurately adjusted. For example, in the overlapping part of the electric field and thermal field concentration regions, a balance and coordination are made according to the optimization objectives of the two, avoiding the deterioration of the other due to the unilateral optimization of the electric field or thermal field, ensuring that while improving the electric field distribution, excessive thermal effects are not caused, and vice versa. At the same time, during the optimization process, the key parameters of the electric field and thermal field are continuously monitored, such as the uniformity of the electric field intensity distribution, the temperature rise index of the thermal field, and the changes in the electrical performance parameters of the overall device (such as on-resistance, breakdown voltage, etc.). According to these monitoring data, the doping concentration and distribution are dynamically fine-tuned, and the optimization scheme is continuously iterated to achieve the best balance state of the electric field and thermal field distributions in the drift region, enabling the MOSFET device to minimize the negative impacts brought by electric field concentration and thermal field concentration under complex operating conditions such as high voltage and large current, improving the reliability, stability, and overall performance of the device, and providing a solid technical guarantee for the wide application of silicon carbide high-voltage MOSFETs in the field of power electronics.

[0031] In a possible implementation manner, step S200 further includes:

[0032] Step S210: Define the doping concentration range and the doping distribution formula. The doping distribution formula is an exponential decay model, and the expression is as follows: .

[0033] Where is the doping concentration at position , is any spatial position coordinate in the length direction of the drift region, is the maximum doping concentration of the doping concentration range, located on the side close to the source electrode in the MOSFET device model, is the decay rate affecting the doping concentration, which is used to describe the trend of the doping concentration decaying exponentially with the distance x.

[0034] Perform uniform doping according to the doping concentration range and the doping profile formula to obtain the initial doping profile.

[0035] Specifically, clarify the doping concentration range. The determination of this range is the basis for the entire initial doping profile and is not arbitrarily set. Instead, it requires comprehensive consideration of various factors. On the one hand, the unique physical and chemical properties of the silicon carbide material itself need to be fully considered. Its high breakdown electric field, high electron mobility and other characteristics impose internal limitations and requirements on the doping concentration. For example, too high a doping concentration may affect the excellent electrical properties of the silicon carbide material, leading to some negative effects such as a decrease in carrier mobility. On the other hand, the expected electrical performance goals of the device need to be combined, such as the on-resistance, breakdown voltage and other indicators that are expected to be achieved. These indicators are closely related to the doping concentration. Through the analysis of a large amount of experimental data and the reference of theoretical research results, a doping concentration range that both conforms to the characteristics of the silicon carbide material and meets the device performance expectations is accurately defined. Determine the doping profile formula. The exponential decay model is used as the doping profile formula. , in this formula Expression position of the doping concentration, where is the arbitrary spatial position coordinate in the direction of the drift region length, which can accurately describe the change of the doping concentration at different positions in the drift region. is the maximum doping concentration in the doping concentration range, and its position is set on the source side close to the MOSFET device model. This setting has important electrical significance. A higher doping concentration is required near the source to ensure effective carrier injection, thereby forming a good conductive channel and ensuring that the device can conduct current smoothly in the on state. And the parameter is used to control the attenuation rate of the doping concentration with respect to the distance , which determines how fast the doping concentration decreases from the source. By reasonably adjusting the value, the precise regulation of the doping concentration distribution pattern in the entire drift region can be achieved, making it meet the expected electrical performance requirements. Here is the base of the natural logarithm of the exponential function. In mathematics, the exponential function (where is the independent variable) is used to describe an exponential growth or decay relationship with as the base. In this doping profile formula, it reflects the trend of the doping concentration decaying exponentially with respect to the distance , that is, as increases, the doping concentration decays according to of The law of the power gradually decreases. This characteristic of exponential decay enables the doping concentration to change smoothly in the drift region starting from the source electrode, meeting the specific requirements of the doping concentration distribution for the optimization of the internal electric field distribution of the silicon carbide high-voltage MOSFET, and contributing to more precise control and adjustment in subsequent simulations of the electric field and thermal field distributions and the entire optimization process to achieve the goal of improving device performance.

[0036] After determining the above parameters, the drift region is uniformly doped according to the doping distribution formula. For each position in the length direction of the drift region , calculate according to the formula, and then dope the entire drift region at this concentration to obtain an initial doping distribution that follows the exponential decay law. This distribution serves as the basis for subsequent TCAD tool simulations, laying the starting conditions for the entire optimization process. Subsequent optimization operations start from this point and are gradually adjusted and improved to achieve the ultimate goal of optimizing the electric field distribution and enhancing device performance.

[0037] In a possible implementation manner, step S400 further includes:

[0038] Step S410: Obtain the first initial doping concentration and the first gradient width.

[0039] Step S420: Invoke the doping distribution formula, and the doping optimization module performs linear gradient doping on the electric field concentration region according to the first initial doping concentration and the first gradient width, and identify the electric field strength of the electric field concentration region.

[0040] Step S430: If the uniformity of the distribution of the electric field strength in the electric field concentration region is less than the preset uniformity, perform iterative simulation with the preset uniformity as the optimization goal, and output the first optimized initial doping concentration and the first optimized gradient width.

[0041] Step S440: Output the first doping optimization distribution according to the first optimized initial doping concentration and the first optimized gradient width.

[0042] Specifically, obtaining the first initial doping concentration and the first gradient width is the starting point of the entire electric field concentration area optimization process. The determination of the first initial doping concentration requires comprehensive consideration of multiple factors, including the electric field intensity distribution in the current electric field concentration area, the electric field basis formed by the previously initialized doping distribution, and the ultimately desired electric field uniformization effect. This concentration value cannot be too high or too low. Too high may cause excessive impact on the electric field distribution in other areas, and too low may not effectively improve the electric field concentration problem. It is a preliminary estimate based on an in-depth understanding of the electrical characteristics of silicon carbide high-voltage MOSFETs and the analysis of similar optimization cases and experimental data in the past. The first gradient width is also crucial, and its selection should be based on the actual range size, shape characteristics, and electric field environment of the electric field concentration area and the surrounding area. If the gradient width is too narrow, it may not be able to fully cover the electric field concentration area for effective optimization; if it is too wide, it may cause too much interference to unnecessary areas, affecting the coordination of the overall electric field distribution. The determination of this width requires accurate electric field simulation and careful analysis of the device structure to ensure that the gradient doping can play a role in the appropriate spatial range.

[0043] Call the pre-designed doping profile formula. Meanwhile, based on the obtained first initial doping concentration and the first gradient width, the doping optimization module begins to perform targeted processing on the electric field concentration region. The first initial doping concentration, as the starting concentration value of doping, determines the initial doping level when doping starts in the electric field concentration region. It is a key parameter set based on a preliminary judgment of the electric field condition in this region and the expected optimization effect. The first gradient width, on the other hand, defines the spatial range and change rate of the doping concentration variation in the electric field concentration region. It determines over what area and at what speed the doping concentration will be adjusted starting from the initial doping concentration. The determination of its size requires precise consideration of the specific characteristics and optimization objectives of the electric field concentration region. After the preparatory work is ready, the doping optimization module performs a detailed linear gradient doping operation in the electric field concentration region according to the established first initial doping concentration and the first gradient width. During this process, for each tiny spatial position in the electric field concentration region, according to its relative distance from the set gradient starting position and in combination with the first gradient width, the doping concentration that should be at this position is accurately calculated through the doping profile formula. Then, precise doping operations are carried out at the corresponding positions according to the calculation results, so as to realize that starting from the starting position, the doping concentration gradually adjusts along the electric field concentration region according to the law of linear variation. This linear gradient doping method can effectively form a smoothly transitional doping concentration distribution in the electric field concentration region, avoiding local electric field fluctuations or other adverse electrical effects that may be caused by sudden changes in the doping concentration, and laying a foundation for improving the uniformity of the electric field distribution. While performing the linear gradient doping, the doping optimization module uses electric field monitoring technology to identify the electric field strength in the electric field concentration region in real time. By arranging a series of highly sensitive virtual electric field sensors in the electric field concentration region, these sensors can accurately sense the magnitude of the electric field strength and transmit the data back to the system in real time. Using professional algorithms to process and analyze the large amount of electric field strength data collected, not only can the specific values of the electric field strength at each position be obtained, but also the distribution profile of the electric field strength in the entire electric field concentration region can be depicted, including the peak and valley values of the electric field strength and their positions, as well as key information such as the change trend of the electric field strength in different directions. These detailed and accurate electric field strength data provide an indispensable basis for subsequent judgment of the electric field distribution uniformity, and also help to deeply understand the actual impact of the linear gradient doping operation on the electric field distribution in the electric field concentration region, so as to further adjust and optimize the doping parameters when necessary to ensure a significant improvement in the uniformity of the electric field strength distribution, and promote the optimization work of the internal electric field distribution of the silicon carbide high-voltage MOSFET to steadily move forward towards the expected goal.

[0044] Once it is determined that the uniformity of the electric field intensity distribution in the electric field concentration area is less than the preset uniformity, the preset uniformity is immediately used as a clear optimization target to start the iterative simulation process. First, the uniformity evaluation algorithm is used to accurately quantify the uniformity of the current electric field intensity distribution. The discrete degree index of the electric field intensity in the entire electric field concentration area is calculated. The specific operation is to first divide the electric field concentration area into several small sub-areas, select multiple sampling points in each sub-area, and measure the electric field intensity values ​​of these sampling points. Then, the variance of these electric field intensity values ​​is calculated. The larger the variance, the more uneven the electric field intensity distribution; the smaller the variance, the closer to uniform distribution. When the calculated variance is greater than the preset uniformity threshold, the system determines that the current electric field distribution is not in an ideal state and needs to be optimized, thereby officially starting the iterative simulation process. Entering the iterative simulation stage, each iteration will deeply analyze the difference relationship between the current electric field intensity distribution data and the preset uniformity, and based on this, the first initial doping concentration and the first gradient width are intelligently adjusted. When adjusting the first initial doping concentration, a comprehensive judgment is made based on the specific situation of the electric field intensity distribution. If it is found that the electric field strength in some areas of the electric field concentration area is too high, resulting in poor overall uniformity, the first initial doping concentration is appropriately reduced according to the pre-set adjustment rules. For example, according to a certain proportional coefficient (the coefficient is determined by a large amount of experimental data and theoretical analysis), the value of the first initial doping concentration is correspondingly reduced according to the degree to which the electric field strength exceeds the ideal range. The principle of this is that reducing the initial doping concentration can reduce the number of carriers in the area, thereby reducing the electric field strength and making the electric field distribution more uniform. At the same time, for the adjustment of the first gradient width, the system will also operate according to the characteristics of the electric field strength distribution. If it is found that the electric field strength changes too drastically in a certain direction, that is, the gradient is too large, affecting the overall uniformity, the first gradient width is increased. By expanding the spatial range of the gradient change, the doping concentration changes more slowly in this direction, and then the change of the electric field strength also tends to be moderate, improving the uniformity of the electric field distribution. During the adjustment process, the electric field strength distribution is continuously recalculated and compared with the preset uniformity to evaluate the adjustment effect.

[0045] The first optimized initial doping concentration is used as the starting reference value for the change in the doping concentration in the electric field concentration region. It determines the initial concentration level when doping starts in this region and provides a basic starting point for constructing the entire doping profile. The first optimized gradient width precisely defines the range and rate of change of the doping concentration in space starting from the starting point. It determines how the doping concentration will be gradually adjusted within the electric field concentration region, just like planning a path that stipulates the direction and rhythm of the concentration change. Then, using the pre-set doping profile formula, the first optimized initial doping concentration and the first optimized gradient width are substituted into it, and the doping concentration is accurately calculated for each tiny spatial position within the electric field concentration region. This formula is constructed based on a deep understanding of the physical characteristics and electrical behavior of the silicon carbide high-voltage MOSFET and can accurately describe the complex relationship between the doping concentration and the spatial position. During the calculation process, for any point within the electric field concentration region, according to its relative distance from the starting position and the first optimized gradient width, the doping concentration value that this point should have is obtained according to the calculation rules specified by the formula. By performing such calculations for all points within the region one by one, a continuous and smoothly varying doping concentration profile within the electric field concentration region is constructed. Finally, the calculated doping concentration profile within the entire electric field concentration region is output as the first doping optimization profile. This profile is the result of careful optimization. It fully considers the special electrical requirements of the electric field concentration region and effectively improves the uniformity of the electric field intensity distribution in this region by reasonably adjusting the doping concentration. The first doping optimization profile will serve as an important part of the subsequent co-optimization in the drift region and, together with other relevant optimization profiles, further improve the overall electric field distribution uniformity of the silicon carbide high-voltage MOSFET, laying a solid foundation for improving the performance, reliability, and stability of the device.

[0046] In a possible implementation manner, step S500 further includes:

[0047] Step S510: Obtain the second initial doping concentration and the second gradient width.

[0048] Step S520: Invoke the doping profile formula, and the doping optimization module performs linear gradient doping on the thermal field concentration region according to the second initial doping concentration and the second gradient width, and identifies the temperature rise index of the thermal field concentration region.

[0049] Step S530: If the temperature rise index of the thermal field concentration region is less than the preset temperature rise index, perform iterative simulation with the preset temperature rise index as the optimization target, and output the second optimized initial doping concentration and the second optimized gradient width.

[0050] Step S540: Output the second doping optimization profile according to the second optimized initial doping concentration and the second optimized gradient width.

[0051] Specifically, obtaining the second initial doping concentration and the second gradient width is the primary link in the optimization of the thermal field concentration region. The determination of the second initial doping concentration requires comprehensive consideration of various factors. It not only needs to combine the current heating situation in the thermal field concentration region, but also consider the thermal properties of the silicon carbide material and the desired thermal equilibrium effect. For example, based on an in-depth understanding of the thermophysical parameters such as the thermal conductivity and specific heat capacity of the material, and the analysis of the initial temperature rise data in the thermal field concentration region, a doping concentration value that can effectively affect the thermal field without causing other negative effects is initially set. The determination of the second gradient width is equally crucial, and its size depends on the geometric shape of the thermal field concentration region, the characteristics of the heat flow distribution, and the thermal coupling relationship with the surrounding regions. If the thermal field concentration region is in a long and narrow shape and the heat flow mainly conducts in a certain direction, then the setting of the second gradient width in this direction needs to be more precise to ensure that the gradient doping can accurately act on the thermal field concentration region, achieve effective thermal field control, and avoid problems such as local overheating or uneven heat diffusion caused by improper doping.

[0052] Call the pre-designed doping distribution formula. Meanwhile, the doping optimization module prepares for the treatment of the heat field concentration region based on the obtained second initial doping concentration and second gradient width. The second initial doping concentration is set as the starting concentration of the doping process in the heat field concentration region, and its value is determined based on various factors such as the initial thermal state of the heat field concentration region, the thermal properties of the silicon carbide material, and the expected thermal field optimization effect. The second gradient width defines the spatial scale and change trend of the doping concentration variation in the heat field concentration region. Its magnitude is closely related to the shape of the heat field concentration region, the heat conduction direction, and the surrounding thermal environment, ensuring that the gradient doping can accurately adapt to the characteristics of the heat field concentration region and effectively regulate the heat field distribution. After the preparation work is ready, the doping optimization module performs a detailed linear gradient doping operation in the heat field concentration region according to the established second initial doping concentration and second gradient width. During this process, for each tiny spatial position in the heat field concentration region, according to its relative distance from the set gradient starting position and combined with the second gradient width, the doping concentration that should be at this position is accurately calculated through the doping distribution formula. Then, precise doping operations are carried out at the corresponding positions according to the calculation results, so as to realize that starting from the starting position, the doping concentration gradually adjusts along the heat field concentration region according to the law of linear change. This linear gradient doping method can form a doping concentration change in the heat field concentration region that matches the heat field distribution, avoiding problems such as local thermal runaway or abnormal heat conduction caused by sudden changes in doping concentration, and laying a foundation for subsequent improvement of the heat field distribution. While performing the linear gradient doping, the doping optimization module uses advanced heat field monitoring technology to identify the temperature rise index of the heat field concentration region in real time. By arranging a series of high-precision temperature sensors in the heat field concentration region, these sensors can accurately sense the tiny changes in temperature and transmit the data in real time. By processing and analyzing the large amount of temperature data collected, not only can the real-time temperature values of each position in the heat field concentration region be obtained, but also key information such as the temperature rise rate, the position of the highest temperature rise point, the average temperature rise, and the uniformity of the temperature distribution can be calculated. These detailed and accurate temperature rise index data provide an indispensable basis for subsequent judgment of the heat field distribution uniformity and evaluation of the heat field optimization effect. At the same time, it also helps to deeply understand the actual impact of the linear gradient doping operation on the heat field distribution in the heat field concentration region, so as to further adjust and optimize the doping parameters when necessary, ensuring the effective improvement of the temperature rise index in the heat field concentration region ultimately, and promoting the optimization work of the internal heat field distribution of the silicon carbide high-voltage MOSFET to steadily move forward towards the expected goal.

[0053] When the temperature rise index in the concentrated heat field area is monitored to be less than the preset temperature rise index, it indicates that the current heat field state has not achieved the expected optimization effect, and the doping scheme needs to be further adjusted. The preset temperature rise index is a key threshold set based on the thermal stability requirements and reliable operating conditions of the silicon carbide high-voltage MOSFET, which reflects the temperature rise limit that the concentrated heat field area can withstand under ideal working conditions. At this time, immediately start the iterative simulation process with this preset temperature rise index as the clear optimization goal, aiming to find the best combination of doping parameters that can meet the requirements of the temperature rise index through multiple rounds of optimization. Entering the iterative simulation stage, each time an iteration is performed, the doping optimization module will deeply analyze the heat field distribution data and temperature rise index information obtained from the previous simulation. Through a complex thermal model, accurately evaluate the influence mechanism of the current second initial doping concentration and the second gradient width on the heat field temperature rise. Then, based on these analysis results, use an intelligent optimization algorithm to adjust these two parameters. For example, if it is found that the temperature rise in a certain part of the concentrated heat field area is too fast, exceeding the expected temperature rise rate, appropriately reduce the second initial doping concentration near this area, because a lower doping concentration can reduce the additional heat generation caused by doping in this area, thereby reducing the temperature rise rate. At the same time, the system may also adjust the second gradient width to make the change of the doping concentration in this area more gentle, avoiding local overheating caused by uneven heat field distribution due to sharp changes in the doping concentration. After each parameter adjustment, perform the doping operation again and recalculate the temperature rise index in the concentrated heat field area based on the new doping distribution. Then, carefully compare the new temperature rise index with the preset temperature rise index to evaluate the optimization effect. As the number of iterations increases, the temperature rise index will gradually approach the preset value. After multiple cycles of simulation, analysis, and adjustment processes, when the temperature rise index reaches or exceeds the preset temperature rise index, the iterative process ends. The second initial doping concentration and the second gradient width determined at this time are the second optimized initial doping concentration and the second optimized gradient width after optimization. These optimized parameters can ensure that the temperature rise index in the concentrated heat field area meets the preset requirements, provide an accurate and reliable parameter basis for the subsequent output of the second doping optimization distribution, strongly promote the optimization process of the internal heat field distribution of the silicon carbide high-voltage MOSFET, and make it develop towards a more stable and reliable thermal performance direction, improving the stability and service life of the device in high-temperature working environments, and meeting the requirements of power electronic systems for high-performance and high-reliability semiconductor devices.

[0054] Based on the second optimized initial doping concentration and the second optimized gradient width determined by the previous iterative optimization, the construction of the second doping optimization distribution begins. First, the second optimized initial doping concentration is taken as the starting point for the change in the doping concentration in the heat field concentration region. It determines the basic concentration level when doping starts in this region and plays a crucial guiding role in the doping concentration distribution of the entire heat field concentration region. The second optimized gradient width precisely defines the spatial change range and rate of the doping concentration from the starting point. Its magnitude and direction are set according to the geometric shape of the heat field concentration region, the characteristics of the heat flow distribution, and the thermal coupling relationship with the surrounding regions, ensuring that the change in the doping concentration can match the heat field distribution and effectively regulate the heat field. Using the pre-set doping distribution formula, the second optimized initial doping concentration and the second optimized gradient width are substituted into it, and the doping concentration is precisely calculated for each tiny spatial position within the heat field concentration region. This doping distribution formula is established based on in-depth research on the thermal properties, heat conduction mechanism, and doping effect of silicon carbide materials, and can accurately describe the complex relationship between the doping concentration and the heat field distribution. During the calculation process, for any point within the heat field concentration region, according to its relative distance from the starting position and the second optimized gradient width, the doping concentration value that the point should have is obtained according to the calculation rules specified by the formula. By performing such calculations for all points in the region one by one, a doping concentration distribution that is continuous, smoothly varying, and in line with the heat field optimization goal within the heat field concentration region is constructed. Finally, the calculated doping concentration distribution within the entire heat field concentration region is output as the second doping optimization distribution. This distribution is the result of careful optimization. It fully considers the special thermal requirements of the heat field concentration region and effectively improves the temperature rise index and the uniformity of the heat field distribution in this region by reasonably adjusting the doping concentration. The second doping optimization distribution will serve as an important part of the subsequent co-optimization in the drift region, cooperate with the first doping optimization distribution, and jointly act on the overall optimization of the drift region. It helps to further improve the thermal stability and reliability of the silicon carbide high-voltage MOSFET, lays a solid foundation for improving the performance of the device under high-temperature and high-power working conditions, and strongly promotes the optimization process of the internal electric field and heat field distribution of the entire silicon carbide high-voltage MOSFET towards a more ideal goal, ensuring that the device can operate stably and efficiently in a complex working environment and meet the growing demands of modern power electronics technology for high-performance semiconductor devices.

[0055] In a possible implementation manner, step S500 further includes:

[0056] Step S550: After co-optimizing in the drift region according to the first doping optimization distribution and the second doping optimization distribution, obtain the optimized doping distribution.

[0057] Step S560: Re-enter the optimized doping distribution into the MOSFET device model to detect whether there are regions of electric field concentration and regions of thermal field concentration.

[0058] Step S570: If the detection in the MOSFET device model includes regions of electric field concentration and regions of thermal field concentration, feedback-update the first optimized doping distribution and the second optimized doping distribution according to the regions of electric field concentration and regions of thermal field concentration.

[0059] Specifically, based on the first optimized doping distribution and the second optimized doping distribution, collaborative optimization is carried out in the drift region to obtain the optimized doping distribution. First, comprehensively consider the complex coupling relationship between the electric field and the thermal field in the drift region, as well as the structural characteristics of the drift region itself and the properties of the silicon carbide material. Deeply explore how the electric field affects the generation of Joule heat in the thermal field, and how the thermal field reacts on the electric field distribution by changing the material parameters. At the same time, combine factors such as the geometric shape and size of the drift region to lay the foundation for collaborative optimization. Use a multi-physics field coupling calculation model, input the first and second optimized doping distributions into it, and according to the physical equations of the electric field and the thermal field, such as the Poisson equation and the heat conduction equation, while considering the carrier continuity equation and the relationship between material parameters and field changes, finely adjust the doping concentration in the drift region. During the calculation process, dynamically balance the optimization requirements of the electric field and the thermal field. For example, in the region of electric field concentration, optimize the electric field distribution by adjusting the doping concentration to reduce heat generation. At the same time, in the region of thermal field concentration, use the adjustment of doping concentration to improve heat conduction to optimize the electric field distribution. After multiple iterative calculations, the electric field and the thermal field reach the best balance state in the drift region. The finally determined optimized doping distribution fully takes into account the uniformity of the electric field strength and the control of the thermal field temperature rise, effectively improves the breakdown voltage capability and thermal stability of the drift region, reduces the on-resistance, and enhances the overall reliability of the device. This distribution will serve as the key basis for subsequent evaluation and further optimization, strongly promoting the high-performance operation of silicon carbide high-voltage MOSFETs in high-voltage and high-power applications, meeting the strict requirements of power electronics technology for high-performance semiconductor devices, pointing the way for the entire device optimization process, and helping it to continuously develop towards the ideal state.

[0060] Precisely re - input the optimized doping profile obtained after co - optimization into the constructed MOSFET device model. This process requires ensuring the accuracy and integrity of the data, enabling the model to perform subsequent calculations and analyses based on the new doping profile information. After the input is completed, a series of initialization settings and parameter adjustments are made to the MOSFET device model to adapt it to the new doping profile state, making full preparations for accurately detecting the electric field concentration region and the thermal field concentration region. Then, using professional electric - field and thermal - field analysis software tools, a comprehensive and detailed scan is performed on the MOSFET device model input with the optimized doping profile. In terms of electric - field distribution detection, by solving the Poisson equation and the current continuity equation based on the new doping profile, the electric - field strength values at various positions within the model are calculated. These calculations involve the precise simulation of the motion behavior of electrons and holes in regions with different doping concentrations, charge distribution, and electric - field interactions. At the same time, during the thermal - field distribution detection process, based on the thermophysical parameters such as the thermal conductivity and specific heat capacity of the material, and considering the heat - conduction and heat - convection mechanisms of Joule heat generated by current passing through the device, the temperature values at each point within the model are calculated, thus obtaining the complete thermal - field distribution information. After obtaining the distribution data of the electric field and the thermal field, according to the pre - set judgment criteria, it is determined whether there are electric - field concentration regions and thermal - field concentration regions within the model. For the electric - field concentration region, by comparing the electric - field strength at each point with the set electric - field strength threshold, if it is found that the electric - field strength in certain regions is significantly higher than that in the surrounding regions and exceeds the threshold, it is determined as an electric - field concentration region; for the thermal - field concentration region, similarly, by comparing the temperature at each point with the pre - set temperature threshold, if there are regions with excessively high local temperatures, it is identified as a thermal - field concentration region. Finally, the detection results are accurately output, clearly indicating whether the model contains electric - field concentration regions and thermal - field concentration regions, providing a basis for possible further optimization in the future. If these concentration regions are detected, it will trigger the next step to feedback and update the first optimized doping profile and the second optimized doping profile; if not detected, it can be initially considered that the current optimization scheme has made the electric - field and thermal - field distributions of the device reach a relatively ideal state, and it can be decided whether to conduct further optimization evaluation or end the optimization process according to actual needs.

[0061] After determining the existence of the electric field concentration region and the thermal field concentration region in the MOSFET device model, the characteristics of these regions are deeply analyzed. For the electric field concentration region, the details of the electric field strength distribution and its coupling relationship with the surrounding area are carefully studied, and the optimization direction is clarified as reducing the peak value of the excessive electric field strength, flattening its change gradient, and coordinating the overall electric field distribution; for the thermal field concentration region, the temperature rise index and the heat conduction characteristics are comprehensively analyzed, and the optimization goal is determined as reducing the temperature at the over-high temperature rise point, balancing the temperature rise rate, and improving the heat accumulation situation. Based on the above analysis, the first doping optimization distribution is feedback updated. The doping concentration is appropriately reduced at the peak value of the electric field strength to reduce the carrier accumulation, the gradient width is adjusted according to the change gradient of the electric field strength to make the change gentle, and the surrounding doping concentration is slightly adjusted considering the coupling relationship; at the same time, for the thermal field concentration region, the doping concentration in the corresponding region is reduced or adjusted according to the temperature rise situation, the gradient change is optimized, and the heat diffusion ability is enhanced. For example, the doping concentration difference with the low-temperature region is adjusted to promote heat conduction. After the careful adjustment of the first and second doping optimization distributions is completed, a new doping optimization scheme is formed, and it is prepared to return to the previous step based on this, and the collaborative optimization is carried out again in the drift region to start a new round of iterative optimization cycle. This process is continuously repeated to continuously improve the doping distribution until the termination condition is met, so that the electric field and thermal field distributions of the device reach the optimum, improving the performance, reliability and stability, and meeting the requirements of high-voltage and high-power applications.

[0062] In a possible implementation manner, step S300 further includes:

[0063] Step S310: Determine whether the electric field concentration region and the thermal field concentration region output by the MOSFET device model include an overlapping concentration region.

[0064] Step S320: If the overlapping concentration region is not included, output the electric field concentration region and the thermal field concentration region, where the distribution uniformity of the electric field strength in the electric field concentration region is greater than or equal to a preset uniformity, and the temperature rise index in the thermal field concentration region is greater than or equal to a preset temperature rise index.

[0065] Specifically, by performing detailed data analysis and spatial position comparison on the electric field concentration region and the thermal field concentration region output by the MOSFET device model, it is determined whether there is an overlapping concentration region between the two. This process involves the precise interpretation of the electric field and thermal field distribution data, as well as the in-depth study of the region boundaries and internal characteristics. Using data analysis tools, the positions and ranges of the electric field concentration region and the thermal field concentration region in three-dimensional space are visually presented to more intuitively observe their relationship. At the same time, the intersection situation of the two in space and the correlation between the electric field strength and temperature changes in the intersection region are calculated, which is used as an important basis for judging whether there is an overlapping concentration region.

[0066] When it is determined that the overlapping concentrated area is not included, the electric field concentrated area and the thermal field concentrated area are further evaluated to ensure that they meet the requirements of specific performance indicators. For the electric field concentrated area, the distribution uniformity of its electric field intensity needs to be greater than or equal to the preset uniformity. This means that the distribution of the electric field in this area is relatively smooth, without obvious sudden changes or local excessive electric field intensity. By calculating statistical parameters such as the variance and gradient of the electric field intensity and comparing them with the preset uniformity index, the uniformity of the electric field concentrated area is verified. For the thermal field concentrated area, its temperature rise index must be greater than or equal to the preset temperature rise index, indicating that the temperature rise situation of the thermal field in this area is within an acceptable range and will not cause device performance degradation or damage due to overheating. By monitoring parameters such as the temperature rise rate, the maximum temperature value, and the uniformity of the temperature distribution in the thermal field concentrated area and comparing them with the preset temperature rise index, it is ensured that the thermal field performance meets the requirements. Only when both the electric field concentrated area and the thermal field concentrated area meet their respective performance indicators can they be accurately output, providing accurate area ranges and performance basic information for subsequent separate optimizations of the electric field and the thermal field, so that more targeted optimization measures can be taken to improve the overall performance and reliability of the silicon carbide high-voltage MOSFET and ensure its stable operation in high-voltage and high-power application scenarios.

[0067] In a possible implementation manner, step S310 further includes:

[0068] Step S311: If the overlapping concentrated area is included, identify the electric field-thermal field coupling influence coefficient.

[0069] Step S312: Obtain the first doping optimization distribution and the second doping optimization distribution corresponding to the overlapping concentrated area.

[0070] Step S313: Couplingly update the first doping optimization distribution and the second doping optimization distribution corresponding to the overlapping concentrated area according to the electric field-thermal field coupling influence coefficient.

[0071] Specifically, when it is determined that there is an overlapping concentrated area in the electric field concentrated area and the thermal field concentrated area output by the MOSFET device model, the identification work of the electric field-thermal field coupling influence coefficient is first carried out. The determination of this coefficient needs to comprehensively consider multiple complex factors, including the interaction intensity between the electric field and the thermal field in the overlapping area, the change of the transport characteristics of carriers under the combined action of the electric field and temperature, and the physical property evolution of the silicon carbide material in the electric field-thermal field coupling environment. By applying the multi-physics field coupling analysis theory and numerical calculation methods, the influence of the change of the electric field intensity on heat generation and heat conduction and the change of electrical parameters such as carrier mobility and diffusion coefficient caused by temperature change are deeply studied, so as to accurately quantify the coupling degree between the electric field and the thermal field and obtain an accurate coupling influence coefficient.

[0072] In order to obtain the first doping optimization distribution and the second doping optimization distribution corresponding to the overlapping region, it is necessary to clarify the specific position information of the overlapping region in the entire MOSFET device model. By analyzing in detail the electric field concentration region and the thermal field concentration region in the model, the spatial range occupied by the overlapping part of the two is determined, such as the key position parameters such as the starting point and ending point coordinates in each dimension. Then, for the first doping optimization distribution data set obtained through the previous complex calculation and optimization process, which contains the doping concentration values set for different positions in the electric field concentration region. According to the determined position of the overlapping region, select the corresponding part of the data from this huge data set, and check one by one whether the position corresponding to each data is within the overlapping region range. If so, extract it and gradually construct the first doping optimization distribution data set for the overlapping region. The same operation is also applied to the acquisition process of the second doping optimization distribution data. Among the existing data related to thermal field optimization, accurately find the doping concentration data belonging to the overlapping region according to the position information of the overlapping region, and organize and summarize it to obtain the complete second doping optimization distribution corresponding to the overlapping region. Through such rigorous and meticulous operations, it is ensured that these two key doping optimization distribution data can be obtained accurately and without error, providing a solid and reliable data basis for the subsequent further optimization of the overlapping region, so as to better balance the interaction between the electric field and the thermal field in this region and improve the overall performance and stability of the device.

[0073] Perform a coupled update operation on the first doping optimization distribution and the second doping optimization distribution corresponding to the overlapping concentration region according to the determined electric field-thermal field coupling influence coefficient. Deeply analyze the interaction relationship between the electric field and the thermal field in the overlapping region reflected by this coupling influence coefficient. If the coupling influence coefficient shows that the influence of the electric field on the thermal field is relatively significant, for example, at certain positions, due to the too high electric field intensity, the local heat generation increases sharply, thus affecting the thermal field distribution. Then, when updating the first doping optimization distribution, for these key positions, appropriately reduce the doping concentration to reduce the excessive heat generated by the electric field effect. At the same time, when adjusting the second doping optimization distribution, in the region where the thermal field is concentrated and greatly affected by the electric field, optimize the gradient of the doping concentration so that the heat can be diffused more efficiently and relieve the local overheating phenomenon. On the contrary, if the coupling influence coefficient indicates that the influence of the thermal field on the electric field is more prominent, such as the high temperature causing a large change in the carrier mobility, affecting the uniformity of the electric field. Then, when updating the first doping optimization distribution, fine-tune the doping concentration in the region where the carrier mobility is severely affected to balance the electric field distribution. For the second doping optimization distribution, consider the distribution of the thermal field and appropriately change the doping concentration to minimize the adverse influence of the thermal field on the electric field. Through such detailed and targeted adjustments, comprehensively couple and update the two doping optimization distributions according to the electric field-thermal field coupling influence coefficient, so that the doping distribution in the overlapping concentration region can better adapt to the complex electric field-thermal field coupling environment, effectively improve the performance and stability of the silicon carbide high-voltage MOSFET in this region, and provide a strong guarantee for the efficient operation of the entire device.

[0074] Embodiment 2, based on the same inventive concept as the internal electric field distribution optimization method of the silicon carbide high-voltage MOSFET in the foregoing embodiment, as Figure 2 shown, the present application provides an internal electric field distribution optimization system for a silicon carbide high-voltage MOSFET. The system in the embodiment of the present application and the method embodiment are based on the same inventive concept. Among them, the system includes:

[0075] A MOSFET device model acquisition module 10, configured to establish a three-dimensional model of a silicon carbide high-voltage MOSFET and acquire a MOSFET device model.

[0076] An initial doping distribution input module 20, configured to construct a doping optimization module and input an initial doping distribution according to the doping optimization module.

[0077] A concentration region acquisition module 30, based on a TCAD tool, simulates the electric field distribution and the thermal field distribution of the MOSFET device model according to the initial doping distribution, and acquires the electric field concentration region and the thermal field concentration region in the drift region of the MOSFET device model.

[0078] The first doping optimization distribution output module 40 is configured to introduce gradient doping in the electric field concentration region by the doping optimization module and output the first doping optimization distribution.

[0079] The second doping optimization distribution output module 50 is configured to introduce gradient doping in the thermal field concentration region by the doping optimization module and output the second doping optimization distribution.

[0080] The collaborative optimization module 60 is configured to perform collaborative optimization in the drift region according to the first doping optimization distribution and the second doping optimization distribution.

[0081] Furthermore, the initialization doping distribution input module further includes:

[0082] Define the doping concentration range and the doping distribution formula, where the doping distribution formula is an exponential decay model, and the expression is as follows: .

[0083] Where is the doping concentration at position , is any spatial position coordinate in the length direction of the drift region, is the maximum doping concentration in the doping concentration range, located on the side close to the source in the MOSFET device model, is the decay rate affecting the doping concentration, used to describe the trend of the doping concentration decaying exponentially with distance x. Perform uniform doping according to the doping concentration range and the doping distribution formula to obtain the initialization doping distribution.

[0084] Furthermore, the first doping optimization distribution output module 40 further includes:

[0085] The first gradient width acquisition unit is configured to acquire the first initial doping concentration and the first gradient width.

[0086] The electric field strength identification unit is configured to call the doping distribution formula, and the doping optimization module performs linear gradient doping on the electric field concentration region according to the first initial doping concentration and the first gradient width, and identify the electric field strength in the electric field concentration region.

[0087] The first optimization gradient width output unit is configured to perform iterative simulation with the preset uniformity as the optimization target if the distribution uniformity of the electric field strength in the electric field concentration region is less than the preset uniformity, and output the first optimized initial doping concentration and the first optimized gradient width.

[0088] The first doping optimization distribution output unit is configured to output the first doping optimization distribution according to the first optimized initial doping concentration and the first optimized gradient width.

[0089] Further, the second doping optimization distribution output module 50 further includes:

[0090] A second gradient width acquisition unit for acquiring a second initial doping concentration and a second gradient width.

[0091] A temperature rise index identification unit for invoking the doping distribution formula, and the doping optimization module performs linear gradient doping on the heat field concentration region according to the second initial doping concentration and the second gradient width, and identifies the temperature rise index of the heat field concentration region.

[0092] A second optimized gradient width output unit for performing iterative simulation with the preset temperature rise index as the optimization target if the temperature rise index of the heat field concentration region is less than the preset temperature rise index, and outputting a second optimized initial doping concentration and a second optimized gradient width.

[0093] A second doping optimization distribution output unit for outputting a second doping optimization distribution according to the second optimized initial doping concentration and the second optimized gradient width.

[0094] Further, the second doping optimization distribution output module 50 further includes:

[0095] A collaborative optimization unit for performing collaborative optimization in the drift region according to the first doping optimization distribution and the second doping optimization distribution, and obtaining an optimized doping distribution.

[0096] A heat field concentration region detection unit for re-inputting the optimized doping distribution into the MOSFET device model to detect whether it includes an electric field concentration region and a heat field concentration region.

[0097] A feedback update unit for, when the MOSFET device model detects that it includes an electric field concentration region and a heat field concentration region, performing feedback update on the first doping optimization distribution and the second doping optimization distribution according to the electric field concentration region and the heat field concentration region.

[0098] Further, the concentration region acquisition module 30 further includes:

[0099] An overlapping concentration region determination unit for determining whether the electric field concentration region and the heat field concentration region output by the MOSFET device model include an overlapping concentration region.

[0100] A concentration region output unit for outputting an electric field concentration region and a heat field concentration region if there is no overlapping concentration region, wherein the distribution uniformity of the electric field intensity in the electric field concentration region is greater than or equal to a preset uniformity, and the temperature rise index of the heat field concentration region is greater than or equal to a preset temperature rise index.

[0101] Further, the overlapping concentrated area determination unit further includes:

[0102] An electric field-thermal field coupling influence coefficient identification unit, configured to identify the electric field-thermal field coupling influence coefficient if the overlapping concentrated area is included.

[0103] A doping optimization distribution acquisition unit, configured to acquire a first doping optimization distribution and a second doping optimization distribution corresponding to the overlapping concentrated area.

[0104] A coupling update unit, configured to perform coupling update on the first doping optimization distribution and the second doping optimization distribution corresponding to the overlapping concentrated area according to the electric field-thermal field coupling influence coefficient.

[0105] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above description of specific embodiments of this specification has been made. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0106] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0107] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A method for optimizing the internal electric field distribution of a silicon carbide high-voltage MOSFET, characterized in that: The method comprises: Establish a three-dimensional model of silicon carbide high-voltage MOSFET and obtain the MOSFET device model; Constructing a doping optimization module, and inputting an initialization doping distribution according to the doping optimization module; Based on the TCAD tool, the electric field distribution and thermal field distribution of the MOSFET device model are simulated according to the initialization doping distribution to obtain the electric field concentration area and thermal field concentration area of ​​the drift region in the MOSFET device model; The doping optimization module introduces gradient doping in the electric field concentration region and outputs a first doping optimization distribution; The doping optimization module introduces gradient doping in the thermal field concentration area and outputs a second doping optimization distribution; Performing collaborative optimization in the drift region according to the first optimized doping distribution and the second optimized doping distribution; Entering the initialization doping profile includes: The doping concentration range and doping distribution formula are defined. The doping distribution formula is an exponential decay model, and the expression is as follows: in, is the doping concentration at position x, x is any spatial position coordinate along the length direction of the drift region, is the maximum doping concentration in the doping concentration range, located on the side close to the source in the MOSFET device model, To affect the decay rate of doping concentration, Used to describe the exponential decay of doping concentration with distance x; Perform uniform doping according to the doping concentration range and the doping distribution formula to obtain an initialization doping distribution; The doping optimization module introduces gradient doping in the electric field concentration area and outputs a first doping optimization distribution, and the method includes: Acquiring a first initial doping concentration and a first gradient width; The doping distribution formula is called, and the doping optimization module performs linear gradient doping on the electric field concentration area according to the first initial doping concentration and the first gradient width, and identifies the electric field strength of the electric field concentration area; If the distribution uniformity of the electric field intensity in the electric field concentration area is less than the preset uniformity, performing iterative simulation with the preset uniformity as the optimization target, and outputting a first optimized initial doping concentration and a first optimized gradient width; outputting a first doping optimization distribution according to the first optimized initial doping concentration and the first optimized gradient width; The doping optimization module introduces gradient doping in the thermal field concentration area and outputs a second doping optimization distribution, and the method includes: Acquire a second initial doping concentration and a second gradient width; The doping distribution formula is called, and the doping optimization module performs linear gradient doping on the thermal field concentration area according to the second initial doping concentration and the second gradient width, and identifies a temperature rise index of the thermal field concentration area; If the temperature rise index of the heat field concentration area is less than the preset temperature rise index, iterative simulation is performed with the preset temperature rise index as the optimization target, and a second optimized initial doping concentration and a second optimized gradient width are output; A second doping optimization distribution is outputted according to the second optimized initial doping concentration and the second optimized gradient width.

2. The method according to claim 1, characterized in that After collaboratively optimizing the drift region according to the first optimized doping distribution and the second optimized doping distribution, obtaining an optimized doping distribution; Re-inputting the optimized doping distribution into the MOSFET device model to detect whether the electric field concentration area and the thermal field concentration area are included; The MOSFET device model includes an electric field concentration area and a thermal field concentration area, and the first doping optimization distribution and the second doping optimization distribution are feedback updated according to the electric field concentration area and the thermal field concentration area.

3. The method according to claim 1, characterized in that The method for obtaining the electric field concentration region and the thermal field concentration region of the drift region in the MOSFET device model includes: Determining whether the electric field concentration region and the thermal field concentration region output by the MOSFET device model include overlapping concentration regions; If the overlapping concentrated area is not included, the electric field concentrated area and the thermal field concentrated area are output, wherein the distribution uniformity of the electric field strength of the electric field concentrated area is greater than or equal to the preset uniformity, and the temperature rise index of the thermal field concentrated area is greater than or equal to the preset temperature rise index.

4. The method according to claim 3, characterized in that If the overlapping concentrated area is included, identify the electric field-thermal field coupling influence coefficient; Acquire a first doping optimization distribution and a second doping optimization distribution corresponding to the overlapping concentrated area; The first doping optimization distribution and the second doping optimization distribution corresponding to the overlapping concentration area are coupled and updated according to the electric field-thermal field coupling influence coefficient.

5. Internal electric field distribution optimization system of silicon carbide high voltage MOSFET, characterized in that: The system is used to execute the internal electric field distribution optimization method of the silicon carbide high-voltage MOSFET according to any one of claims 1 to 4, and the system comprises: MOSFET device model acquisition module, used to establish a three-dimensional model of silicon carbide high-voltage MOSFET and obtain the MOSFET device model; An initialization doping distribution input module is used to construct a doping optimization module and input the initialization doping distribution according to the doping optimization module; A concentrated area acquisition module, which performs electric field distribution and thermal field distribution simulation on the MOSFET device model according to the initialization doping distribution based on a TCAD tool, and obtains an electric field concentrated area and a thermal field concentrated area in the drift region of the MOSFET device model; A first doping optimization distribution output module, used for the doping optimization module to introduce gradient doping in the electric field concentration area and output a first doping optimization distribution; A second doping optimization distribution output module, used for the doping optimization module to introduce gradient doping in the thermal field concentration area and output a second doping optimization distribution; A collaborative optimization module is used to perform collaborative optimization in the drift region according to the first doping optimization distribution and the second doping optimization distribution.

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