Surface acoustic wave device model coefficient extraction and simulation method and apparatus, and related device

Abstract

The present invention relates to the technical field of wireless communications. Disclosed are a surface acoustic wave device model coefficient extraction and simulation method and apparatus, and a related device. The method comprises the following steps: step S1, acquiring admittance parameters of a batch of surface acoustic wave devices; step S2, removing abnormal waveforms and random noise in measured curves by means of a filtering and noise reduction algorithm, so as to obtain true values of the measured curves; step S3, searching for feature information in the measured curves by using a moving window of a preset size in combination with a preset limiting condition; step S4, on the basis of a feature change relationship between the coefficient of a phenomenological model and each of the conductance of a conductance curve and the susceptance of a susceptance curve, sequentially optimizing all model coefficients step by step; and step S5, using a neural network model to develop a simulation method for surface acoustic wave devices within a preset structural dimension range and obtain a simulation result. The surface acoustic wave device model coefficient extraction and simulation method of the present invention can improve the accuracy of surface acoustic wave device simulation.

Description

Surface acoustic wave device model coefficient extraction and simulation method, device and related equipment TECHNICAL FIELD

[0001] The present application relates to the field of wireless communication technology, in particular to a surface acoustic wave device model coefficient extraction and simulation method, device and related equipment. BACKGROUND

[0002] At present, the surface acoustic wave filter has good temperature stability, working stability, design flexibility, and at the same time, has the advantages of small size, light weight, low power consumption, etc., and is widely used in communication, radar, television, radio and other fields. The surface acoustic wave device is composed of an upper metal structure and a lower piezoelectric substrate. Different metal structure designs correspond to different impedance characteristics. With the development of communication technology, the surface acoustic wave filter is continuously developing towards high frequency, low loss, high power bearing, etc. In the development process of new products, high-quality iteration not only can win the first opportunity, but also can reduce the cost.

[0003] In related technologies, there are characteristic waveforms in the admittance curve of the surface acoustic wave device, that is, accurate description of these characteristics can accurately simulate the electrical characteristics of the device; the impedance characteristics of the same structure of the devices of different manufacturers have slight differences, but they are enough to affect the performance of the device. Therefore, using different model coefficients obtained from different measured data of different manufacturers can make the simulation more accurate.

[0004] However, since the simulation development needs a large amount of measured data of surface acoustic wave filters with different structure sizes, random noise is inevitably introduced, and at the same time, it is also possible to introduce clutter to pollute the true value. Therefore, the industry often uses HCT (hierarchical cascade technology) to fit and obtain the phenomenological model coefficient based on the non-polluted data of HCT. However, this method is fitted twice, and the precision is inevitably lost. SUMMARY

[0005] The purpose of the embodiments of the present application is to provide a surface acoustic wave device model coefficient extraction and simulation method to solve the problem of low simulation precision of the existing surface acoustic wave device.

[0006] In order to solve the above technical problems, in a first aspect, the embodiments of the present application provide a surface acoustic wave device model coefficient extraction and simulation method, which comprises the following steps:

[0007] Step S1, obtaining the admittance parameters of a batch of surface acoustic wave devices;

[0008] Step S2, processing the corresponding measured curve according to the admittance parameters, and removing the abnormal waveform and random noise in the measured curve through a filtering and denoising algorithm to obtain the true value of the measured curve;

[0009] Step S3, searching the characteristic information in the measured curve through a preset size of a moving window combined with a preset limit condition; wherein, the measured curve includes a conductance curve and a susceptance curve;

[0010] Step S4, establishing an empirical model, and according to the characteristic change relationship between the coefficients of the empirical model and the conductance of the conductance curve and the susceptance of the susceptance curve, step by step optimizing all the model coefficients;

[0011] Step S5, after obtaining a batch of the model coefficients, taking a preset geometric size as input and the model coefficients as output, training to obtain a neural network model, and using the neural network model to develop a simulation method of a surface acoustic wave device in a preset structure size range and obtain a simulation result.

[0012] Preferably, in the step S2, the filtering and denoising algorithm includes any one or a combination of multiple kinds of median filtering, wavelet analysis, upper envelope algorithm, Fourier transform, least square and B-spline fitting.

[0013] Preferably, in the step S3, the preset size of the moving window is a moving window of 2MHz-10MHz.

[0014] Preferably, in the step S3, the preset limit condition is that the difference between the maximum value and the minimum value of the conductance in the moving window satisfies a preset value.

[0015] Preferably, in the step S4, the model coefficients include propagation loss, relative dielectric constant, two-parameters, electromechanical coupling coefficient and velocity adjustment coefficient of the surface acoustic wave device.

[0016] Preferably, the preset geometric size is a film thickness ratio and a metallization rate of the surface acoustic wave device.

[0017] In a second aspect, an embodiment of the present application provides a surface acoustic wave device model coefficient extraction and simulation device, comprising:

[0018] An acquisition module is configured to acquire admittance parameters of a batch of surface acoustic wave devices.

[0019] A removal module is configured to process corresponding measured curves according to the admittance parameters, remove abnormal waveforms and random noise in the measured curves through a filtering and denoising algorithm, and obtain true values of the measured curves.

[0020] A search module is configured to search characteristic information in the measured curves through a preset size of a moving window combined with a preset limit condition; wherein, the measured curve includes a conductance curve and a susceptance curve.

[0021] An optimization module is configured to establish a phenomenological model, and to step by step and sequentially optimize all model coefficients according to a characteristic variation relationship between coefficients of the phenomenological model and conductance of a conductance curve and susceptance of a susceptance curve respectively;

[0022] A training module is configured to obtain a neural network model by training with preset geometric dimensions as input and the model coefficients as output, and to develop a simulation method of a surface acoustic wave device in a preset structure size range and obtain a simulation result by using the neural network model.

[0023] In a third aspect, an embodiment of the present application provides a computer device, which comprises a memory, a processor, and a surface acoustic wave device model coefficient extraction and simulation program stored in the memory and executable on the processor, and the processor implements the steps in the surface acoustic wave device model coefficient extraction and simulation method when executing the surface acoustic wave device model coefficient extraction and simulation program.

[0024] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores a surface acoustic wave device model coefficient extraction and simulation program, and the steps in the surface acoustic wave device model coefficient extraction and simulation method are implemented when the surface acoustic wave device model coefficient extraction and simulation program is executed by a processor.

[0025] Compared with the prior art, the surface acoustic wave device model coefficient extraction and simulation method in the present application realizes the purpose of obtaining a quasi-true value from original measurement data by filtering and denoising the measured data by using the filtering and denoising algorithm in steps S1-S5, and effectively improves the simulation accuracy of the surface acoustic wave device by using a moving window with a proper size and a limiting condition to accurately obtain a feature point; and the universality of simulation is greatly improved by using the neural network model. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort, and the drawings comprise:

[0027] FIG. 1 is a flowchart of a surface acoustic wave device model coefficient extraction and simulation method provided by an embodiment of the present application;

[0028] FIG. 2 is a schematic diagram of a measured curve containing a double-peak abnormal feature point provided by an embodiment of the present application;

[0029] FIG. 3 is a schematic diagram of a corrected structure provided by an embodiment of the present application;

[0030] Fig. 4 is a schematic diagram of a key feature point search result provided by an embodiment of the present application;

[0031] Fig. 5a is a schematic diagram of a simulation result comparison without step S2 provided by an embodiment of the present application;

[0032] Fig. 5b is a schematic diagram of a simulation result comparison without step S2 provided by an embodiment of the present application;

[0033] Fig. 6a is a schematic diagram of a simulation result comparison with step S2 provided by an embodiment of the present application;

[0034] Fig. 6b is a schematic diagram of a simulation result comparison with step S2 provided by an embodiment of the present application;

[0035] Fig. 7 is a module diagram of a surface acoustic wave device model coefficient extraction and simulation device provided by an embodiment of the present application;

[0036] Fig. 8 is a module diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0038] Embodiment one

[0039] Please refer to Figs. 1-6b, an embodiment of the present application provides a surface acoustic wave device model coefficient extraction and simulation method, which comprises the following steps:

[0040] Step S1, obtaining admittance parameters of a batch of surface acoustic wave devices.

[0041] In the present example, several resonators of different sizes and a DMS filter are included, wherein the resonators are used for model coefficient extraction, and the filter DMS is used for simulation verification. Generally, random noise exists in the measured data, and there may also be clutter, which affects the accuracy of the main resonance characteristics.

[0042] Specifically, the admittance parameters of a batch of surface acoustic wave devices are obtained by a probe station. As shown in Fig. 2, it is an amplitude-frequency characteristic curve of a certain resonator obtained by a probe station test, wherein the admittance decibel and the conductance decibel are shown respectively. The conductance decibel curve of a certain feature (the maximum point near 780M) is disturbed by clutter to form a double peak.

[0043] Step S2, a corresponding measured curve is processed according to the admittance parameter, and abnormal waveforms and random noise in the measured curve are removed through a filtering denoising algorithm to obtain a true value of the measured curve.

[0044] Specifically, the blue curve in FIG. 3 is the unprocessed conductance decibel curve in FIG. 2, and the yellow curve in FIG. 3 is the conductance decibel curve processed through a median filter, Fourier transform, inverse Fourier transform, spline fitting algorithm, etc. It can be seen that the double peak in step S1 has been processed into a single peak, which conforms to the general case. At the same time, according to the conductance measurement results and the correction results, through random noise removal, the curve is smoothed.

[0045] Step S3, search for feature information in the measured curve through a moving window of a preset size combined with a preset limited condition; wherein the measured curve includes a conductance curve and a susceptance curve. The feature information is related to the positions of a plurality of maximum values in the measured curve, and the feature information obtained in this step is used for obtaining model coefficients in subsequent step S4.

[0046] Step S4, establish an empirical model, and according to the characteristic change relationship between the coefficients of the empirical model and the conductance of the conductance curve and the susceptance of the susceptance curve, all model coefficients are optimized step by step.

[0047] Among them, the empirical model is a commonly used model of surface acoustic wave devices, such as COM model, etc.

[0048] Step S5, after obtaining a batch of model coefficients, taking a preset geometric size as input and the model coefficients as output, a neural network model is obtained through training, and a simulation method of a surface acoustic wave device within a preset structure size range and a simulation result obtained are developed by using the neural network model.

[0049] In specific implementation, through the above steps S1-S5, the measured data is de-waved and smoothed by using the filtering denoising algorithm, thereby achieving the purpose of obtaining a quasi-true value from the original measurement data, and the algorithm of accurately obtaining feature points by using a moving window of appropriate size combined with a limiting condition effectively improves the simulation accuracy of the surface acoustic wave device; the application of the neural network model greatly improves the universality of the simulation.

[0050] In this embodiment, in step S2, the filtering denoising algorithm includes any one or a combination of multiple of median filtering, wavelet analysis, upper envelope algorithm, Fourier transform, least squares and B-spline fitting.

[0051] In this embodiment, in step S3, the moving window of the preset size is a moving window of 2MHz-10MHz.

[0052] In the step S3, the preset restriction condition is that the difference between the maximum value and the minimum value of the conductance in the moving window satisfies a preset value. The preset value can be that the difference between the maximum value and the minimum value in the moving window is greater than or less than the preset value, which is not described herein.

[0053] Specifically, a 10MHz-sized moving window is used, and the difference between the maximum value and the minimum value in the window is limited to be less than a preset extreme point. There is a 10MHz-sized window under each small window, and the difference between the maximum value and the minimum value in the moving process is less than a preset value. As shown in FIG. 4, the feature points are the maximum extreme point and the extreme points on the left and right sides thereof.

[0054] The preset value is 3-5dB, and optionally, the preset value is 3dB.

[0055] In the step S4, the model coefficients include the propagation loss, the relative dielectric constant, the two-parameter, the electromechanical coupling coefficient and the velocity adjustment coefficient of the surface acoustic wave device.

[0056] Specifically, the phenomenological model is used, and the simulation and the feature information difference in the step S2 are used as the objective function, so that the propagation loss, the relative dielectric constant, the two-parameter, the electromechanical coupling coefficient and the velocity adjustment coefficient are optimized. A large number of model coefficients of resonators of different sizes are further optimized and used for training of the neural network model in the subsequent step S5.

[0057] In the embodiment, the preset geometric size is the film thickness ratio and the metallization ratio of the surface acoustic wave device.

[0058] The film thickness ratio usually refers to the ratio of the change of temperature to the change of film thickness in a system, and is one of important physical quantities of film material properties and an indication of filter performance. The metallization ratio has an important influence on the frequency characteristics and electrical characteristics of the interdigital transducer.

[0059] Specifically, the neural network model is established with the film thickness ratio and the metallization ratio of the surface acoustic wave device as inputs and the model coefficients as outputs, and the model coefficients and the size information in the step S4 are used for model training. The neural network model is used to develop a surface acoustic wave device electrical characteristic prediction program. As shown in FIGS. 5a, 5b, 6a and 6b, the simulation comparison of s11 and s21 curves of a 5-order DMS is shown. It can be known from the simulation comparison that the overall predicted feature positions are relatively accurate, and the simulation results are more accurate after the abnormal measured curves are processed by the step S2.

[0060] Embodiment Two

[0061] As shown in FIG. 7, the embodiment of the present application provides a device model coefficient extraction and simulation device 200, comprising:

[0062] The acquisition module 201 is configured to acquire admittance parameters of a batch of surface acoustic wave devices.

[0063] The removal module 202 is configured to process a corresponding measured curve according to the admittance parameters, and remove abnormal waveforms and random noise in the measured curve by a filtering and noise reduction algorithm to obtain a true value of the measured curve.

[0064] The search module 203 is configured to search for feature information in the measured curve by a preset size of a moving window in cooperation with a preset limitation condition; wherein the measured curve comprises a conductance curve and a susceptance curve.

[0065] The optimization module 204 is configured to establish a phenomenological model, and step by step optimize all model coefficients according to a characteristic change relationship between coefficients of the phenomenological model and conductance of the conductance curve and susceptance of the susceptance curve.

[0066] The training module 205 is configured to, after obtaining a batch of the model coefficients, take a preset geometric size as input and the model coefficients as output, train a neural network model, and develop a simulation method of a surface acoustic wave device in a preset structure size range and obtain a simulation result by using the neural network model.

[0067] Specifically, the acquisition module 201 is configured to acquire admittance parameters of a batch of surface acoustic wave devices; the removal module 202 is configured to process a corresponding measured curve according to the admittance parameters, and remove abnormal waveforms and random noise in the measured curve by a filtering and noise reduction algorithm to obtain a true value of the measured curve; the search module 203 is configured to search for feature information in the measured curve by a preset size of a moving window in cooperation with a preset limitation condition; the optimization module 204 is configured to establish a phenomenological model, and step by step optimize all model coefficients according to a characteristic change relationship between coefficients of the phenomenological model and conductance of the conductance curve and susceptance of the susceptance curve; and the training module 205 is configured to, after obtaining a batch of the model coefficients, take a preset geometric size as input and the model coefficients as output, train a neural network model, and develop a simulation result of a surface acoustic wave device in a preset structure size range by using the neural network model. In this way, the filtering and noise reduction algorithm is used to remove clutter and smooth the measured data, thereby achieving the purpose of obtaining a quasi-true value from original measurement data. Meanwhile, the algorithm of using a moving window with a proper size in cooperation with a limitation condition accurately obtains feature points, thereby effectively improving simulation accuracy of the surface acoustic wave device. The application of the neural network model greatly improves the universality of simulation.

[0068] The function realized by the above-mentioned acoustic surface wave device model coefficient extraction and simulation device 200 is the same as the above-mentioned acoustic surface wave device model coefficient extraction and simulation method, and the same technical effect is obtained, which will not be described here.

[0069] In the removing module 202, the filtering and noise reduction algorithm includes any one or a combination of multiple of median filtering, wavelet analysis, upper envelope algorithm, Fourier transform, least square and B-spline fitting.

[0070] In the searching module 203, the preset size of the moving window is a 10MHz moving window.

[0071] In the searching module 203, the preset limit condition is that the difference between the maximum value and the minimum value of the moving window is less than a preset value.

[0072] Specifically, a 10MHz moving window is used, and the difference between the maximum value and the minimum value of the window is limited to be less than a preset extreme point, and the extreme point meeting the requirement is found. Each small window has a 10MHz window, and the difference between the maximum value and the minimum value is less than a preset value during the movement. As shown in FIG. 3, the feature points are the maximum extreme point and the extreme points on the left and right sides thereof.

[0073] The preset value is 3-5dB, and optionally, the preset value is 3dB.

[0074] In the optimization module 204, the model coefficient includes the propagation loss, the relative dielectric constant, the two-parameter, the electromechanical coupling coefficient and the velocity adjustment coefficient of the acoustic surface wave device.

[0075] In the optimization module 204, the preset geometric size is the film thickness ratio and the metallization rate of the acoustic surface wave device.

[0076] Embodiment three

[0077] As shown in FIG. 8, the embodiment of the present application provides a computer device 300, which includes a memory 301, a processor 302, and an acoustic surface wave device model coefficient extraction and simulation program stored in the memory 301 and executable on the processor 302. When the processor 302 executes the acoustic surface wave device model coefficient extraction and simulation program, the steps in the above-mentioned acoustic surface wave device model coefficient extraction and simulation method are realized.

[0078] Step S1, obtaining the admittance parameters of a batch of acoustic surface wave devices;

[0079] Step S2, processing the corresponding measured curve according to the admittance parameters, removing the abnormal waveform and random noise in the measured curve through a filtering and noise reduction algorithm, and obtaining the true value of the measured curve.

[0080] Step S3, searching the characteristic information in the measured curve by a preset size of a moving window and a preset limited condition; wherein, the measured curve includes a conductance curve and a susceptance curve;

[0081] Step S4, establishing an empirical model, and according to the characteristic change relation between the coefficients of the empirical model and the conductance of the conductance curve and the susceptance of the susceptance curve, step by step optimizing all the model coefficients;

[0082] Step S5, after obtaining the batch of the model coefficients, taking a preset geometric size as input and the model coefficients as output, training to obtain a neural network model, and developing a simulation method of a surface acoustic wave device in a preset structure size range and obtaining a simulation result by using the neural network model.

[0083] Embodiment four

[0084] The embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium stores a surface acoustic wave device model coefficient extraction and simulation program. The surface acoustic wave device model coefficient extraction and simulation program is executed by a processor to realize the steps in the surface acoustic wave device model coefficient extraction and simulation method.

[0085] It should be noted that in this document, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that processes, methods, articles or devices including a series of elements not only include those elements, but also include other elements not explicitly listed, or further include elements inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "including a" does not exclude the presence of additional identical elements in the process, method, article or device including the element.

[0086] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment method can be realized by means of software and necessary general hardware platform, of course, it can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes a plurality of instructions for making a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) execute the method described in each embodiment of the present application.

[0087] The above merely illustrates the embodiments of the present application, and is not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process conversion, or direct or indirect application in other related technical fields, which is made according to the content of the present application, shall be included in the patent protection scope of the present application.

Claims

1. A method for extracting and simulating model coefficients of a surface acoustic wave device, characterized by, The method comprises the following steps: Step S1, obtaining admittance parameters of a batch of surface acoustic wave devices; Step S2, processing a corresponding measured curve according to the admittance parameters, and removing abnormal waveforms and random noise in the measured curve by a filtering and noise reduction algorithm to obtain a true value of the measured curve; Step S3, searching for feature information in the measured curve by a preset size moving window combined with a preset limit condition; wherein the measured curve includes a conductance curve and a susceptance curve; Step S4, establishing an empirical model, and step by step optimizing all model coefficients according to a characteristic change relationship between coefficients of the empirical model and conductance of the conductance curve and susceptance of the susceptance curve; Step S5, after obtaining a batch of the model coefficients, taking a preset geometric size as input and the model coefficients as output, training to obtain a neural network model, and developing a simulation method of a surface acoustic wave device within a preset structure size range and obtaining a simulation result by using the neural network model.

2. The method of claim 1, wherein, In the step S2, the filtering and noise reduction algorithm includes any one or a combination of multiple of median filtering, wavelet analysis, upper envelope algorithm, Fourier transform, least squares and B-spline fitting. 3.The method of claim 1, wherein, In the step S3, the preset size moving window is a 2MHz-10MHz moving window.

4. The method of claim 1, wherein, In the step S3, the preset limit condition is that a difference between a maximum value and a minimum value of the conductance in the moving window satisfies a preset value.

5. The method of claim 1, wherein, In the step S4, the model coefficients include propagation loss, relative dielectric constant, two parameters, electromechanical coupling coefficient and velocity adjustment coefficient of the surface acoustic wave device.

6. The method of SAW device model coefficient extraction and simulation according to claim 1, wherein, The preset geometric size is a film thickness ratio and a metallization rate of the surface acoustic wave device.

7. A device for extracting and simulating model coefficients of surface acoustic wave devices, characterized in that, Comprise: An acquisition module is configured to obtain admittance parameters of a batch of surface acoustic wave devices; A removal module is configured to process a corresponding measured curve according to the admittance parameters, and remove abnormal waveforms and random noise in the measured curve by a filtering and noise reduction algorithm to obtain a true value of the measured curve; A search module is configured to search for feature information in the measured curve by a preset size moving window combined with a preset limit condition; wherein the measured curve includes a conductance curve and a susceptance curve; An optimization module is configured to establish an empirical model, and step by step optimize all model coefficients according to a characteristic change relationship between coefficients of the empirical model and conductance of the conductance curve and susceptance of the susceptance curve; A training module is configured to, after obtaining a batch of the model coefficients, take a preset geometric size as input and the model coefficients as output, train to obtain a neural network model, and develop a simulation method of a surface acoustic wave device within a preset structure size range and obtain a simulation result by using the neural network model.

8. A computer device, comprising: Comprise: A memory, a processor, and a surface acoustic wave device model coefficient extraction and simulation program stored on the memory and executable on the processor, wherein the processor implements the steps of the surface acoustic wave device model coefficient extraction and simulation method according to any one of claims 1-6 when executing the surface acoustic wave device model coefficient extraction and simulation program.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a surface acoustic wave device model coefficient extraction and simulation program, and the surface acoustic wave device model coefficient extraction and simulation program, when executed by the processor, implements the steps in the surface acoustic wave device model coefficient extraction and simulation method according to any one of claims 1-6.

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