CN112651203B - Parameter optimization method and device, server and storage medium - Google Patents
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- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
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- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
- G06F30/373—Design optimisation
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
Abstract
The present disclosure relates to the field of microelectronic device modeling, and provides a parameter optimization method and apparatus for a device modeling tool, a server, and a storage medium, wherein by extracting simulation parameters of a semiconductor device model, the simulation parameters are electrical characteristic curves obtained by the semiconductor device model under the same setting conditions that measurement parameters are obtained by the semiconductor device model and an actual semiconductor device in the device modeling tool; then selecting a characteristic data point set to be optimized in the simulation parameters, wherein the characteristic data point is a discrete point of the simulation parameters deviating from the measurement parameters under the same coordinate system; and determining a target characteristic data point set by using an optimization algorithm based on the measurement parameters, wherein the characteristic value of the target characteristic data point is the target characteristic value of the characteristic data point to be optimized after optimization. Therefore, the efficiency and the accuracy of simulation modeling can be effectively improved.
Description
Technical Field
The present disclosure relates to the field of microelectronic device modeling, and in particular, to a parameter optimization method and apparatus for a device modeling tool, a server, and a storage medium.
Background
The semiconductor device design greatly benefits from the use of simulation and models, and the simulation can partially replace the silicon wafer experiment which consumes cost, so that the cost can be reduced, the development period can be shortened, and the yield can be improved. That is, the simulation can virtually produce and guide the actual production. Thereby saving time and expense in developing new or expanded prior art. The need for technology development is far more than a basic simulation capability, and instead modeling and optimization tools and methods to help achieve and optimize a design are becoming increasingly important.
Simulation software, like in particular in the electronic IT industry, is very versatile by use. Only the integrated circuit industry is classified into circuit simulation, device simulation, process simulation, and the like. In practical application, the device simulation can realize the electrical characteristic simulation by extracting the electrical parameters of the device model, which is not only beneficial to designing a novel device, but also can be used for improving the old device and verifying the electrical characteristics of the device.
Integrated circuit generic simulation programs (Simulation program with integrated circuit emphasis, SPICE) are the most popular circuit level simulation programs, and different stimuli can be set to obtain the response result of the designed circuit under the condition. SPICE simulation is performed on the semiconductor device, so that simulation results are matched with actual test results of the device.
Electromagnetic compatibility (Electro Magnetic Compatibility, EMC) simulation is a forward electromagnetic compatibility design method based on software analysis, so that engineers can simulate radiation emission of circuits and components, determine whether the emission meets common EMC standards, avoid unnecessary design and achieve the purposes of saving time, cost and the like. The complex components used as main interference sources in EMC simulation often have the characteristics of complex modeling, large influence range and the like.
The current component model modeling mode is based on mathematical modeling and assisted by SPICE language mode, and model parameter sources are mainly obtained from the component parameters given by official documents. However, for the purposes of confidentiality and the like, the published parameters often have small deviation from the actual measured parameters, so that the modeled component model is often unsatisfactory, therefore, the modeling of the semiconductor device needs to be manually adjusted, whether the model parameters are reasonable or not is judged through comparison of the model simulation result and the actual device characteristics, the model parameter values are further modified, manual adjustment is performed for many times until the model parameters are matched with the actual device characteristics, the manual adjustment consumes longer, the modeling efficiency is low, and great waste is caused to human resources.
Therefore, how to optimize the parameters of the component model has very important significance for improving the simulation test efficiency and the accuracy thereof.
Disclosure of Invention
In order to solve the technical problems, the present disclosure provides a parameter optimization method and apparatus for a device modeling tool, a server and a storage medium, which can effectively improve efficiency and accuracy of simulation modeling.
In one aspect, the present disclosure provides a parameter optimization method for a device modeling tool, comprising:
acquiring measurement parameters of the semiconductor device under actual setting conditions, and selecting a semiconductor device model with a corresponding size according to the semiconductor device;
extracting simulation parameters of a semiconductor device model, wherein the simulation parameters are electrical characteristic curves obtained by the semiconductor device model under the same setting conditions that measurement parameters are obtained by the semiconductor device model and an actual semiconductor device in the device modeling tool;
selecting a characteristic data point set to be optimized in the simulation parameters, wherein the characteristic data point is a discrete point of the simulation parameters deviating from the measurement parameters under the same coordinate system;
and determining a target characteristic data point set by using an optimization algorithm based on the measurement parameters, wherein the characteristic value of the target characteristic data point is the target characteristic value after the characteristic data point to be optimized is optimized.
Preferably, before the step of extracting the simulation parameters of the semiconductor device model, the parameter optimization method further includes:
and acquiring the measurement parameters of the actual semiconductor device under the set conditions, and selecting a semiconductor device model with a corresponding size according to the semiconductor device, wherein the selected semiconductor device model is used for extracting the simulation parameters.
Preferably, the step of selecting the semiconductor device model of the corresponding size according to the semiconductor device includes:
and acquiring a semiconductor device model set of different size combinations, and extracting a semiconductor device model corresponding to the semiconductor device size in the semiconductor device model set by adopting a control search type algorithm.
Preferably, the step of extracting simulation parameters of the semiconductor device model includes:
acquiring a plurality of electrical characteristic curves of parameters to be extracted of the semiconductor device model in the device modeling tool under different setting conditions; and
traversing the plurality of electrical characteristic curves by using the conditional statement, and extracting the electrical characteristic curve under the same setting condition as the measurement parameter.
Preferably, a set of the simulation parameters and the measurement parameters extracted under the same setting conditions are displayed in the same coordinate system,
And, the simulation parameters and the measurement parameters extracted under the corresponding multiple setting conditions are displayed in the same coordinate system or multiple coordinate systems.
Preferably, the step of selecting the set of characteristic data points to be optimized in the simulation parameters includes:
selecting one or more rectangular judgment intervals based on a group of simulation parameters and measurement parameters under the same coordinate system;
in each judging interval, calculating the mean square error of the characteristic value of each data point on the simulation parameter corresponding to the measurement parameter;
comparing the mean square error of the characteristic value of each data point with the size of a set threshold value, and marking the data points which are corresponding to the data points exceeding the set threshold value as the characteristic data points;
and selecting a plurality of characteristic data points in one judgment interval or adjacent judgment intervals as a characteristic data point set to be optimized in the simulation parameters.
Preferably, the foregoing optimization algorithm is any one selected from the group consisting of an optimizer algorithm, a levenberg-marquardt algorithm, and a genetic algorithm.
Preferably, the step of determining the set of target characteristic data points using an optimization algorithm based on the measurement parameters includes:
Selecting an optimizer algorithm, and setting optimization parameters, wherein the optimization parameters at least comprise a maximum iteration number, a maximum iteration time and a stepping precision value;
running an optimizer algorithm to perform repeated iterative adjustment on each characteristic data point, and determining a target characteristic data point corresponding to each characteristic data point according to the set threshold value through a comparison result of the mean square deviations; and
and updating the corresponding characteristic data point by each target characteristic data point, and storing and displaying the optimized simulation parameters.
Preferably, the measurement parameter and the simulation parameter each include time and characteristic data corresponding to the time, and the characteristic data includes a voltage value and/or a current value.
In another aspect, the present disclosure provides a parameter optimization apparatus for a device modeling tool, comprising:
the measured data acquisition module is used for reading the measurement parameters of the semiconductor device under the actual setting conditions stored in the configuration file of the device modeling tool;
the simulation data acquisition module is used for selecting a semiconductor device model with a corresponding size according to the semiconductor device in the device modeling tool, and extracting simulation parameters of the semiconductor device model, wherein the simulation parameters are electrical characteristic curves of the device model and the measurement parameters under the same setting conditions;
The optimizing module is used for selecting a characteristic data point set to be optimized in the simulation parameters and determining a target characteristic data point set by utilizing an optimizing algorithm based on the measurement parameters,
the characteristic data points are discrete points, in which the simulation parameters deviate from the measurement parameters, under the same coordinate system, and the characteristic values of the target characteristic data points are target characteristic values after the characteristic data points to be optimized are optimized.
Preferably, the foregoing parameter optimizing apparatus further includes:
the display module is in signal connection with the optimization module and is used for displaying one or more groups of the measurement parameters and the simulation parameters in the same coordinate system or respectively displaying one group of the measurement parameters and the simulation parameters in a plurality of coordinate systems;
the storage module is used for storing a semiconductor device model set with different size combinations in the device modeling tool and a plurality of electrical characteristic curves obtained under different setting conditions of parameters to be extracted in the semiconductor device model under a certain size.
Preferably, the display module is a display screen with an input device or a touch display screen, and the display module is further configured to:
one or more rectangular judgment sections are selected based on a group of simulation parameters and measurement parameters under the same coordinate system.
Preferably, the foregoing optimizing module includes:
the calculating unit is used for calculating the mean square error of the characteristic value of each data point on the simulation parameter corresponding to the measurement parameter in each judging interval;
a judging unit for comparing the mean square error of the characteristic value of each data point with the magnitude of a set threshold value, and marking the data points which are corresponding to the data points exceeding the set threshold value as the characteristic data points;
the processing unit is used for selecting a judgment interval or a plurality of characteristic data points in adjacent judgment intervals as a characteristic data point set to be optimized in the simulation parameters;
an execution unit, which is respectively connected with the judgment unit and the processing unit, is used for selecting an optimization algorithm and setting optimization parameters, running the optimization algorithm to carry out iterative adjustment for a plurality of times on each characteristic data point, determining a target characteristic data point corresponding to each characteristic data point according to the set threshold value through the comparison result of the mean square error,
and the execution unit is further used for updating the corresponding characteristic data point according to each target characteristic data point and storing the optimized simulation parameters in the storage module.
Preferably, the measurement parameter and the simulation parameter each include time and characteristic data corresponding to the time, and the characteristic data includes a voltage value and/or a current value.
In another aspect, the present disclosure further provides a server, including:
a processor;
a memory for storing one or more programs;
wherein the one or more programs are executed by the processor such that the processor implements the parameter optimization method as described above.
In yet another aspect the present disclosure also provides a computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the parameter optimization method as described above.
The beneficial effects of the present disclosure are: the present disclosure provides a parameter optimization method and apparatus for a device modeling tool, a server, and a storage medium, capable of first obtaining a measurement parameter of a semiconductor device under an actual setting condition, and selecting a semiconductor device model of a corresponding size according to the semiconductor device; secondly, extracting simulation parameters of the semiconductor device model, wherein the simulation parameters are electrical characteristic curves of the device model in the device modeling tool under the same set conditions as the measurement parameters; then selecting a characteristic data point set to be optimized in the simulation parameters, wherein the characteristic data point is a discrete point of the simulation parameters deviating from the measurement parameters under the same coordinate system; and determining a target characteristic data point set by using an optimization algorithm based on the measurement parameters, wherein the characteristic value of the target characteristic data point is the target characteristic value of the characteristic data point to be optimized after optimization. According to the parameter optimization method, through the device modeling tool provided with the optimization algorithm, fitting optimization is carried out on simulation parameters under the same setting conditions according to actual measurement parameters, so that the efficiency and accuracy of simulation modeling can be effectively improved, the simulation modeling is more consistent with actual electrical characteristics of the device, the working efficiency of a designer can be improved, repeated labor is reduced, and the modeling period of fitting of the device is shortened.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of the embodiments of the present disclosure with reference to the accompanying drawings.
FIG. 1 is a flow chart of a method for parameter optimization for a device modeling tool according to one embodiment of the present disclosure;
FIG. 2 shows a schematic flow chart of sub-steps of step S30 in the parameter optimization method shown in FIG. 1;
FIG. 3 is a schematic diagram of a parameter optimization apparatus for a device modeling tool according to a second embodiment of the present disclosure;
FIG. 4 is a schematic diagram showing the configuration of an optimizing module in the parameter optimizing apparatus shown in FIG. 3;
FIG. 5 illustrates a schematic diagram of the results of traversing a semiconductor device model assembly to select semiconductor device models of corresponding dimensions but different parameter conditions in one embodiment of the present disclosure;
FIG. 6 is a diagram showing the screening results for screening a parameter condition in one embodiment of the present disclosure;
FIGS. 7a and 7b are schematic diagrams showing judgment intervals in a plurality of electrical characteristic curves under different setting conditions according to an embodiment of the present disclosure;
FIG. 8 is a schematic diagram of a setup interface for optimizing parameters of an optimization module in one embodiment of the disclosure;
fig. 9 shows a schematic structural diagram of a server according to a third embodiment of the present disclosure.
Detailed Description
In order that the disclosure may be understood, a more complete description of the disclosure will be rendered by reference to the appended drawings. Preferred embodiments of the present disclosure are shown in the drawings. This disclosure may, however, be embodied in different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used in the description of the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure.
According to the related art, various devices modeling tools are used, such as SPICE simulation software which is commonly used at present provides a better user interface for facilitating the use of users, the simulation can be performed after components in a simulation library are connected into a schematic diagram (of course, necessary simulation parameters are set), in the device modeling simulation process, the main flow method of the current circuit design is generally based on a device model which describes the characteristics of the device under the small signal working condition and the large signal working condition in an equivalent circuit mode, and the existing component modeling has the following problems: manufacturers are not able to provide accurate device parameters due to technical confidentiality, and technicians need to optimize these parameters, so a device model is a precondition for circuit design using devices. However, in the process of parameter optimization, the problems of large parameter quantity, undefined parameter influence and the like exist, and technicians usually have the problems of complex logic, complex operation, high time cost and the like in the current adjustment process; the effectiveness of the optimization result is difficult to judge, thereby influencing the yield of the circuit design.
The model parameters are manually adjusted in the traditional semiconductor device modeling, whether the model parameters are reasonable or not is judged through comparison of the model simulation result and the actual device characteristics, the model parameter values are further modified, the model parameters are manually adjusted for multiple times until the model parameters are matched with the actual device characteristics, meanwhile, analysis of the characteristic parameters in a complex circuit is more complicated and accuracy is required, great challenges are brought to actual work, the manual adjustment mode is long in time consumption and low in modeling efficiency, and great waste is caused to human resources.
Based on the above, the present disclosure provides a parameter optimization method and apparatus for a device modeling tool, a server and a storage medium, by using the device modeling tool configured with an optimization algorithm, fitting optimization is performed on simulation parameters under the same setting conditions according to actual measurement parameters, so as to effectively improve efficiency and accuracy of simulation modeling.
The present disclosure is described in detail below with reference to the accompanying drawings.
Embodiment one:
fig. 1 is a schematic flow chart of a parameter optimization method for a device modeling tool according to an embodiment of the present disclosure, and fig. 2 is a schematic flow chart of sub-steps of step S30 in the parameter optimization method shown in fig. 1.
Referring to fig. 1 and 2, in one aspect, a method for optimizing parameters for a device modeling tool is provided according to an embodiment of the present disclosure, including:
step S10: and obtaining the measurement parameters of the semiconductor device under the actual setting conditions.
In step S10, for the semiconductor device to be simulated, an electrical characteristic curve for one or more parameters is obtained as a measurement parameter according to the defined parameters in the actual test environment.
According to an exemplary embodiment of the present invention, the measurement parameters and the simulation parameters each include time and characteristic data corresponding to the time. The characteristic data are, for example, voltage values and/or current values. Taking the parameter optimization of an N-type metal oxide semiconductor field effect transistor (Metal Oxide Semiconductor Filed Effect Transistor, simply referred to as an NMOS transistor) model as an example, the actual measurement data may include: temperature t, voltage amplitude vds, vbs and vgs, and current ids.
Step S20: and selecting a semiconductor device model with a corresponding size according to the semiconductor device, and extracting simulation parameters of the semiconductor device model.
In step S20, the simulation parameter is an electrical characteristic curve of the device model in the device modeling tool under the same setting condition as the measurement parameter. A process of selecting the semiconductor device model: and acquiring a semiconductor device model set of different size combinations, and extracting a semiconductor device model corresponding to the semiconductor device size in the semiconductor device model set by adopting a control search type algorithm, as shown in fig. 5. Specifically, for example, the storage file storing the semiconductor device models with different sizes can be traversed based on a screening statement of a certain scripting language, wherein the screening statement comprises keywords or phrases of storage file names with corresponding sizes (parameters), and the semiconductor device models conforming to the sizes are screened out; then, according to the conditional statement of a certain parameter, inquiring and grouping a plurality of electrical characteristic curves of the semiconductor device model conforming to the size under different setting conditions (parameters) to obtain simulation parameters of the measured parameters of the corresponding actual semiconductor device in the semiconductor device model under the setting conditions, as shown in fig. 6;
The step of extracting simulation parameters of the semiconductor device model comprises the following steps: acquiring a plurality of electrical characteristic curves of parameters to be extracted of the semiconductor device model in the device modeling tool under different setting conditions; and traversing the plurality of electrical characteristic curves by using the conditional statement, and extracting the electrical characteristic curve under the same setting condition as the measurement parameter.
Further, a set of the simulation parameters and the measurement parameters extracted under the same setting conditions are displayed in the same coordinate system, and a plurality of sets of the simulation parameters and the measurement parameters extracted under a plurality of setting conditions are displayed in the same coordinate system or a plurality of coordinate systems, as shown in fig. 7a and 7 b. Because the execution of the optimization process is based on the control command and the setting of the optimization parameters, the target data is optimized, and the optimization process can be performed in parallel and synchronously without mutual interference, so that the optimization process can be saved, and the time can be saved.
Step S30: and selecting a characteristic data point set to be optimized in the simulation parameters.
In step S30, the characteristic data points are discrete points where the simulation parameters deviate from the measurement parameters under the same coordinate system. The selection process is to select one or more rectangular judgment intervals (shown as R1 in FIG. 7a and A1, A2 and A3 in FIG. 7 b) based on a set of the simulation parameters and the measurement parameters in the same coordinate system; in each judging interval, calculating the mean square error of the characteristic value of each data point on the simulation parameter corresponding to the measurement parameter; comparing the mean square error of the characteristic value of each data point with the size of a set threshold value, and marking the data points which are corresponding to the data points exceeding the set threshold value as the characteristic data points; and then selecting a judgment interval or a plurality of characteristic data points in adjacent judgment intervals as a characteristic data point set to be optimized in the simulation parameters.
Step S40: and determining a target characteristic data point set by using an optimization algorithm based on the measurement parameters.
In step S40, the characteristic value of the target characteristic data point is the target characteristic value after the characteristic data point to be optimized is optimized. The step of determining the target characteristic data point set by using an optimization algorithm based on the measurement parameters comprises the following steps: selecting an optimizer algorithm, and setting optimization parameters, wherein the optimization parameters at least comprise a maximum iteration number, a maximum iteration time and a stepping precision value; running an optimizer algorithm to perform repeated iterative adjustment on each characteristic data point, and determining a target characteristic data point corresponding to each characteristic data point according to the set threshold value through a comparison result of the mean square deviations; and updating the corresponding characteristic data point by each target characteristic data point, and storing and displaying the optimized simulation parameters. By adopting the parameter optimization method provided by the embodiment, the optimization direction of the semiconductor device is guided by using the actual measurement parameters, so that the effectiveness of the optimization result is ensured.
Further, the foregoing optimization algorithm is not limited to one of the optimizer algorithms, such as the Levenberg-Marquardt algorithm, genetic algorithm, or other algorithms disclosed in the prior art for performing the parameter optimization fit, but is not limited thereto. If a genetic algorithm is used for carrying out unified processing on a characteristic data point set in the simulation parameters, on one hand, the optimal solution of the parameter output result can be achieved through moderate iteration, and approximation fitting is not needed like other methods; on the other hand, the optimization is performed by negating the wrong individual, and the optimization is not forced to be completely consistent with the actual measurement result. The method can solve the problems and automatically, efficiently and accurately realize the parameter optimization of the semiconductor device model.
Embodiment two:
fig. 3 shows a schematic structural diagram of a parameter optimization apparatus for a device modeling tool according to a second embodiment of the present disclosure, and fig. 4 shows a schematic structural diagram of an optimization module in the parameter optimization apparatus shown in fig. 3.
Referring to fig. 3 and 4, in another aspect the present disclosure provides a parameter optimization 100 for a device modeling tool, comprising at least: a simulation data acquisition module 110, a measured data acquisition module 120 and an optimization module 130,
the measured data obtaining module 120 is configured to read measurement parameters of the semiconductor device under actual setting conditions stored in a configuration file of the device modeling tool;
the simulation data obtaining module 110 is configured to select a semiconductor device model with a corresponding size according to the semiconductor device in the device modeling tool, and extract simulation parameters of the semiconductor device model, where the simulation parameters are electrical characteristic curves of the device model and the measurement parameters under the same setting conditions;
the optimization module 130 is configured to select a set of characteristic data points to be optimized in the simulation parameters, and determine a set of target characteristic data points by using an optimization algorithm based on the measurement parameters.
The characteristic data points are discrete points, in which the simulation parameters deviate from the measurement parameters, under the same coordinate system, and the characteristic values of the target characteristic data points are the target characteristic values after the characteristic data points to be optimized are optimized.
Further, the foregoing parameter optimization apparatus 100 further includes: a display module 140 and a storage module 150.
The display module 140 is in signal connection with the optimization module 130, and is configured to display one or more sets of the measurement parameters and the simulation parameters in a same coordinate system, or display one set of the measurement parameters and the simulation parameters in a plurality of coordinate systems respectively;
the storage module 150 is configured to store a set of semiconductor device models with different combinations of sizes in the device modeling tool, and a plurality of electrical characteristic curves of parameters to be extracted in the semiconductor device models with a certain size under different setting conditions.
Further, the display module 140 is a display screen with an input device or a touch display screen, and the display module 140 is further configured to: based on a set of the simulation parameters and the measurement parameters in the same coordinate system, one or more rectangular judgment sections are selected, wherein the selected judgment sections are shown as R1 in FIG. 7a and A1, A2 and A3 in FIG. 7 b.
Further, the foregoing optimizing module 130 includes: a calculation unit 131, a judgment unit 132, a processing unit 133 and an execution unit 134,
the calculating unit 131 is configured to calculate, in each of the determination intervals, a mean square error of a characteristic value of each data point on the simulation parameter corresponding to the measurement parameter;
The judging unit 132 is connected to the calculating unit 131, and is configured to compare the mean square error of the characteristic value of each data point with a set threshold value, and mark the data point corresponding to the data point exceeding the set threshold value as the characteristic data point;
the processing unit 133 is connected to the judging unit 132, and is configured to select a judging section or a plurality of characteristic data points in adjacent judging sections as a characteristic data point set to be optimized in the simulation parameters;
the execution unit 134 is respectively connected to the judging unit 131 and the processing unit 133, and is configured to select an optimization algorithm (such as an optimizer algorithm) and set an optimization parameter (the optimization parameter at least includes a maximum iteration number, a maximum iteration time and a stepping precision value), perform multiple iterative adjustments on each of the characteristic data points by running the optimization algorithm, determine a target characteristic data point corresponding to each of the characteristic data points according to the set threshold value according to the comparison result of the mean square error, and a setting interface of the optimization parameter of the execution unit 134 in the optimization module 130 is shown in fig. 8,
the execution unit 134 is further configured to update the corresponding characteristic data point according to each of the target characteristic data points, and store the optimized simulation parameters in the storage module 150.
Of course, the optimization algorithm in the embodiments of the present disclosure includes, but is not limited to, an optimizer algorithm, and in other alternative embodiments, a levenberg-marquardt algorithm, a genetic algorithm, or others may be selected, where it should be noted that the genetic algorithm and the levenberg-marquardt algorithm are more mature optimization algorithms, and may be understood in conjunction with the related art, and will not be described in detail herein.
For example, a Levenberg-Marquardt algorithm is selected, corresponding derivative values can be obtained for different characteristic curves according to a model function in the process of selecting the judging intervals, different judging intervals are separated according to the change points of the derivative values, and then the characteristic values which enable the function values to be minimum are found by fitting of a function model algorithm in the process of optimizing.
According to an exemplary embodiment of the present invention, the measurement parameters and the simulation parameters each include time and characteristic data corresponding to the time. The characteristic data are, for example, voltage values and/or current values. Taking the parameter optimization of an N-type metal oxide semiconductor field effect transistor (Metal Oxide Semiconductor Filed Effect Transistor, simply referred to as an NMOS transistor) model as an example, the actual measurement data may include: temperature t, voltage amplitude vds, vbs and vgs, and current ids.
Example III
Fig. 9 shows a schematic structural diagram of a server according to a third embodiment of the present disclosure.
Referring to fig. 9, the present disclosure also sets forth a block diagram of an exemplary server suitable for use in implementing embodiments of the present disclosure. It should be understood that the server shown in fig. 9 is only an example, and should not be construed as limiting the functionality and scope of use of the disclosed embodiments.
As shown in fig. 9, the server 200 is in the form of a general purpose computing device. The components of server 200 may include, but are not limited to: one or more processors or processing units 210, a memory 220, a bus 201 that connects the various system components, including the memory 220 and the processing units 210.
Bus 201 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Server 200 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by server 200 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 220 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 221 and/or cache memory 222. Server 200 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 223 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 9, commonly referred to as a "hard disk drive"). Although not shown in fig. 9, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be coupled to bus 201 through one or more data medium interfaces. Memory 220 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the present disclosure.
Programs/utilities 224 having a set (at least one) of program modules 2241 may be stored in, for example, memory 220, such program modules 2241 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 2241 generally perform the functions and/or methods in the embodiments described in the embodiments of the disclosure.
Further, the server 200 may also be communicatively coupled to a display 300 for displaying the results of the screening ranking, and the display 300 may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some embodiments, the display 300 may also be a touch screen.
Further, the server 200 may also communicate with one or more devices that enable a user to interact with the server 200, and/or with any device (e.g., network card, modem, etc.) that enables the server 200 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 230. Also, the server 200 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through a network adapter 240. As shown, network adapter 240 communicates with other modules of server 200 over bus 201. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with server 200, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 210 executes various functional applications and data processing by running programs stored in the system memory 220, for example, implementing the parameter optimization method for a device modeling tool provided in the first embodiment of the present disclosure.
Example IV
A fourth embodiment of the present disclosure also provides a computer-readable storage medium having stored thereon a computer program (or referred to as computer-executable instructions) which, when executed by a processor, is configured to perform a parameter optimization method for a device modeling tool provided in the first embodiment of the present disclosure, the parameter optimization method including:
acquiring measurement parameters of the semiconductor device under actual setting conditions;
selecting a semiconductor device model with a corresponding size according to the semiconductor device, and extracting simulation parameters of the semiconductor device model;
selecting a characteristic data point set to be optimized in the simulation parameters; and
and determining a target characteristic data point set by using an optimization algorithm based on the measurement parameters.
The computer storage media of the embodiments of the present disclosure may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
In summary, the present disclosure provides a parameter optimization method and apparatus, a server, and a storage medium for a device modeling tool, which can perform fitting optimization on simulation parameters under the same setting conditions according to actual measurement parameters by using the device modeling tool configured with an optimization algorithm, so that not only can efficiency and accuracy of simulation modeling be effectively improved, but also work efficiency of a designer can be improved, repeated labor is reduced, and a modeling period of fitting of the device is accelerated.
It should be noted that in the description of the present disclosure, it should be understood that the terms "upper," "lower," "inner," and the like indicate an orientation or a positional relationship, and are merely for convenience of describing the present disclosure and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present disclosure.
Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Finally, it should be noted that: it is apparent that the above examples are merely illustrative of the present disclosure and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. And obvious variations or modifications thereof are contemplated as falling within the scope of the present disclosure.
Claims (14)
1. A method of parameter optimization for a device modeling tool, comprising:
extracting simulation parameters of a semiconductor device model, wherein the simulation parameters are electrical characteristic curves obtained by the semiconductor device model under the same setting conditions that measurement parameters are obtained by the semiconductor device model and an actual semiconductor device in the device modeling tool;
selecting a characteristic data point set to be optimized in the simulation parameters, wherein the characteristic data points are discrete points, deviating from the measurement parameters, of the simulation parameters under the same coordinate system;
determining a target characteristic data point set by using an optimization algorithm based on the measurement parameters, wherein the characteristic value of the target characteristic data point is the target characteristic value of the characteristic data point to be optimized after optimization,
The step of extracting simulation parameters of the semiconductor device model comprises the following steps:
acquiring a plurality of electrical characteristic curves of parameters to be extracted of the semiconductor device model in the device modeling tool under different setting conditions;
traversing the plurality of electrical characteristic curves by using a conditional statement, extracting an electrical characteristic curve under the same setting condition as the measurement parameter, displaying a group of simulation parameters and measurement parameters extracted under the same setting condition in the same coordinate system,
and the step of selecting the characteristic data point set to be optimized in the simulation parameters comprises the following steps:
selecting one or more rectangular judgment intervals based on a group of simulation parameters and measurement parameters under the same coordinate system;
in each judging interval, calculating the mean square error of the characteristic value of each data point on the simulation parameter corresponding to the measurement parameter;
comparing the mean square error of the characteristic value of each data point with the size of a set threshold value, and marking the data points which are corresponding to the data points exceeding the set threshold value as the characteristic data points;
selecting a plurality of characteristic data points in one judging interval or adjacent judging intervals as a characteristic data point set to be optimized in the simulation parameters,
The measurement parameters may include: temperature t, voltage amplitude vds, vbs and vgs, and current ids.
2. The parameter optimization method according to claim 1, wherein, before the step of extracting simulation parameters of the semiconductor device model, the parameter optimization method further comprises:
and acquiring measurement parameters of the actual semiconductor device under the set conditions, and selecting a semiconductor device model with a corresponding size according to the semiconductor device, wherein the selected semiconductor device model is used for extracting the simulation parameters.
3. The parameter optimization method as set forth in claim 2, wherein the step of selecting the semiconductor device model of the corresponding size according to the semiconductor device comprises:
and acquiring a semiconductor device model set of different size combinations, and extracting a semiconductor device model corresponding to the semiconductor device size in the semiconductor device model set by adopting a control search type algorithm.
4. The parameter optimization method according to claim 3, wherein the plurality of sets of the simulation parameters and the measurement parameters extracted under the corresponding plurality of setting conditions are displayed in the same coordinate system or a plurality of coordinate systems.
5. The parameter optimization method according to claim 1, wherein the optimization algorithm is any one selected from an optimizer algorithm, a levenberg-marquardt algorithm, and a genetic algorithm.
6. The parameter optimization method of claim 5, wherein said step of utilizing an optimization algorithm to determine a set of target characteristic data points based on said metrology parameters comprises:
selecting an optimizer algorithm, and setting optimization parameters, wherein the optimization parameters at least comprise a maximum iteration number, a maximum iteration time and a stepping precision value;
operating an optimizer algorithm to perform repeated iterative adjustment on each characteristic data point, and determining a target characteristic data point which accords with the set threshold and corresponds to each characteristic data point according to the comparison result of the mean square error; and
and updating the corresponding characteristic data point by each target characteristic data point, and storing and displaying the optimized simulation parameters.
7. The parameter optimization method according to claim 1, wherein the measurement parameter and the simulation parameter each include time and characteristic data corresponding to time, the characteristic data including a voltage value and/or a current value.
8. A parameter optimization apparatus for a device modeling tool, comprising:
the measured data acquisition module is used for reading the measurement parameters of the semiconductor device under the actual setting conditions stored in the configuration file of the device modeling tool;
The simulation data acquisition module is used for selecting a semiconductor device model with a corresponding size according to the semiconductor device in the device modeling tool, extracting simulation parameters of the semiconductor device model, wherein the simulation parameters are electrical characteristic curves obtained by the device model and the measurement parameters under the same setting conditions;
the optimization module is used for selecting a characteristic data point set to be optimized in the simulation parameters, determining a target characteristic data point set by utilizing an optimization algorithm based on the measurement parameters, wherein the characteristic data points are discrete points of the simulation parameters deviating from the measurement parameters under the same coordinate system, and the characteristic values of the target characteristic data points are target characteristic values of the characteristic data points to be optimized after optimization;
the display module is in signal connection with the optimization module and is used for displaying one or more groups of the measurement parameters and the simulation parameters in the same coordinate system, selecting one or more rectangular judgment intervals based on one group of the simulation parameters and the measurement parameters in the same coordinate system,
wherein the measurement parameters may include: temperature t, voltage amplitude vds, vbs and vgs, and current ids,
And, the optimization module includes:
the calculating unit is used for calculating the mean square error of the characteristic value of each data point on the simulation parameter corresponding to the measurement parameter in each judging interval;
the judging unit is used for comparing the mean square error of the characteristic value of each data point with the size of a set threshold value, and marking the data point which is corresponding to the data point exceeding the set threshold value as the characteristic data point;
and the processing unit is used for selecting a plurality of characteristic data points in one judgment interval or adjacent judgment intervals as a characteristic data point set to be optimized in the simulation parameters.
9. The parameter optimization apparatus of claim 8, wherein the display module is further configured to display a set of the measurement parameters and the simulation parameters in a plurality of coordinate systems, respectively,
and, the parameter optimizing apparatus further includes:
the storage module is used for storing a semiconductor device model set with different size combinations in the device modeling tool and a plurality of electrical characteristic curves of parameters to be extracted in the semiconductor device model under a certain size under different setting conditions.
10. The parameter optimization apparatus of claim 8, wherein the display module is a display screen with an input device or a touch display screen.
11. The parameter optimization apparatus of claim 9, wherein the optimization module further comprises:
an execution unit, which is respectively connected with the judging unit and the processing unit, is used for selecting an optimization algorithm and setting optimization parameters, operating the optimization algorithm to carry out repeated iterative adjustment on each characteristic data point, determining a target characteristic data point corresponding to each characteristic data point according to the set threshold value through the comparison result of the mean square error,
and the execution unit is further used for updating the corresponding characteristic data point according to each target characteristic data point and storing the optimized simulation parameters in the storage module.
12. The parameter optimization device of claim 9, wherein the measurement parameter and the simulation parameter each comprise time and time-dependent characteristic data, the characteristic data comprising a voltage value and/or a current value.
13. A server, comprising:
a processor;
a memory for storing one or more programs;
wherein the one or more programs, when executed by the processor, cause the processor to implement the parameter optimization method of any one of claims 1 to 7.
14. A computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the parameter optimization method according to any one of claims 1 to 7.
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