CN117240249A - Optimization method of hybrid acoustic wave filter - Google Patents
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Abstract
The application discloses an optimization method of a hybrid acoustic wave filter, and belongs to the field of radio frequency filters. The method comprises the following steps: s1, setting target performance parameters of a filter; s2, presetting a topological structure and the branch number of a filter; s3, setting variable individuals, population numbers, maximum iteration times and parameter optimization ranges of the filters; s4, initializing a group; s5, evaluating the fitness; s6, calculating the congestion degree and sequencing the congestion degree and the non-dominance; s7, judging whether the optimal individual meets the termination condition; s8, if the filter structure is met, outputting the filter structure; otherwise, the optimization algorithm updates the population and returns to step S5. The application provides a new thought for designing the topological structure of the filter, reduces the labor and time cost in engineering, and ensures that the design of the acoustic wave filter is not highly dependent on the experience of technicians.
Description
Technical Field
The application relates to the field of radio frequency filters, in particular to an optimization method of a hybrid acoustic wave filter.
Background
With the development of wireless communication technology, radio frequency front end components play an increasingly important role in modern communication systems. In the radio frequency front end, the acoustic wave filter is a very critical component. The clutter and noise in the radio frequency signals are filtered, and the performance and fault tolerance of the wireless communication system are improved, so that the requirements of people on high speed, reliability and safety of communication are better met.
For 4G and 5G technologies in mobile communication, acoustic wave filters are required to have good bandwidths and lower loss coefficients. Meanwhile, the size of the filter is required to be smaller and the power consumption is required to be lower. This places higher demands on the design of the acoustic wave filter, requiring more complex and careful optimization designs.
The design method of the acoustic wave filter is usually software automatic optimization and manual adjustment of engineers. The optimization method which can be selected by the optimization tool of the software is limited and traditional, the setting options are limited, and all the required indexes cannot be perfectly realized without worry in the optimization process. Because of the numerous parameters that need to be adjusted while their relationships are subtle and complex, the process of manual optimization can be very tedious and time consuming, will be heavily dependent on the level of experience of the engineer, requires extensive experimentation and debugging, and has human error and limitations.
Disclosure of Invention
In order to solve at least one of the technical problems existing in the prior art to a certain extent, the application aims to provide an optimization method of a hybrid acoustic wave filter.
The technical scheme adopted by the application is as follows:
an optimization method of a hybrid acoustic wave filter, comprising the steps of:
s1, determining target performance parameters of a filter according to design indexes;
s2, presetting a topological structure and the branch number of a filter;
s3, setting variable individuals, population numbers, maximum iteration times and parameter optimization ranges of the filters;
s4, initializing a group;
s5, calculating the distance between each topological structure and the target performance, and obtaining fitness evaluation;
s6, calculating the crowding degree and performing non-dominant sorting according to the obtained distance between each topological structure and the target performance;
s7, judging whether the optimal individual meets the termination condition;
s8, if the filter structure is met, outputting the filter structure; otherwise, the optimization algorithm updates the population and returns to step S5.
Further, the optimization method is applicable to structural optimization of all filters capable of calculating the ABCD matrix, such as a trapezoidal filter.
Further, the target performance includes at least one of passband bandwidth, insertion loss, out-of-band rejection, in-band ripple, return loss, or in-band standing wave ratio.
Further, the number of branches of the filter is determined according to the difficulty of the target performance, and the greater the target performance difficulty is, the greater the number of branches is required.
Further, the branches of the filter include, but are not limited to, one, more or all of a resonator, a capacitor, an inductance, a series or parallel combination of a resonator and a capacitor, and a series or parallel combination of a capacitor and an inductance.
Further, the variable individuals of the filter may be defined as parameter structures and parameter arrays. Defining a variable individual by using a parameter structure body under the condition that the branch structure is fixed; the parameter array is used to define the variable individuals in the case of an unfixed branch structure.
Further, the number of optimized groups and the maximum number of iterations are determined according to a selected optimization algorithm. And the parameter optimization range is determined according to the actual process condition.
Further, in step S8, the step of updating the population by the optimization algorithm includes:
and updating the population by adopting a moth group flame algorithm.
Further, for the case where the filter structure is known, step S1 is followed directly by step S3, and step S2 is not performed. The step S3 comprises the following steps:
based on the known filter structure and part of the structural parameters, the variable individuals of the filter are constructed to achieve further optimization of the hybrid acoustic wave filter.
Further, the optimizing method further comprises:
taking the filter structure output in the step S8 as a known filter structure, constructing a variable unit of the filter according to the known filter structure, and returning to the step S3.
Compared with the prior art, the application has the following advantages and beneficial effects:
(1) The intelligent optimization algorithm provided by the application has high flexibility. In the use process, the limiting conditions on the processing technology can be set in a program mode for the outside such as principle logic, and the method is also applicable to the local optimization of the filter.
(2) The combined optimization method provided by the application ensures that the optimization of the topological structure is not limited to the dilemma of local optimal solution and incapability of converging, and greatly improves the searching opportunity of the optimal structure.
(3) The application uses the topological structure of a large number of novel acoustic wave filters generated by the intelligent optimization algorithm to provide thought sources for the subsequent design of technicians, so that the acoustic wave filter structure is not limited to a classical scheme, thereby providing possibility for realizing target performance which cannot be achieved by the traditional structure.
(4) The intelligent optimization algorithm reduces the manpower and time cost in engineering, so that the design of the acoustic wave filter is not highly dependent on the experience of technicians. With the help of the optimization algorithm, trial and error cost and repeatability of technicians can be greatly reduced.
(5) The application combines the structure design of the hybrid acoustic wave filter with the intelligent optimization algorithm, provides a new thought of the topology structure design of the filter, can be applied to the acoustic wave filter, is also applicable to filters with other structures, and has a wide application range.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description is made with reference to the accompanying drawings of the embodiments of the present application or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present application, and other drawings may be obtained according to these drawings without the need of inventive labor for those skilled in the art.
FIG. 1 is a flow chart of a method of optimizing a hybrid acoustic wave filter in accordance with an embodiment of the present application;
FIG. 2 is a schematic diagram of a filter parameter structure in an embodiment of the application;
FIG. 3 is a graph showing the comparison of S parameters of the ladder acoustic wave filter of example 1 according to the present application;
FIG. 4 is a graph of the parameter optimization convergence of example 1 in an example of the application;
FIG. 5 is a diagram of an array of filter parameters according to an embodiment of the present application;
FIG. 6 is a schematic diagram of filter branch types in an embodiment of the application;
fig. 7 is a schematic view of a filter structure 1 of embodiment 2 in an embodiment of the present application;
fig. 8 is a schematic diagram of an optimization process S parameter of the filter structure 1 of embodiment 2 in the embodiment of the present application;
fig. 9 is a schematic diagram of a filter structure 2 of embodiment 2 in an embodiment of the present application;
FIG. 10 is a schematic diagram of the optimization process S parameter of the filter structure 2 of embodiment 2 in the embodiment of the present application;
FIG. 11 is a schematic diagram of the parameter optimization convergence curve of embodiment 2 in accordance with an embodiment of the present application;
fig. 12 is a schematic view of a filter structure of embodiment 3 in an embodiment of the present application;
FIG. 13 is a schematic diagram of the S parameter of the filter structure optimization process of embodiment 3 according to the present application;
FIG. 14 is a schematic diagram of the parameter optimization convergence curve of example 3 in an embodiment of the application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the application. The step numbers in the following embodiments are set for convenience of illustration only, and the order between the steps is not limited in any way, and the execution order of the steps in the embodiments may be adaptively adjusted according to the understanding of those skilled in the art.
In the description of the present application, it should be understood that references to orientation descriptions such as upper, lower, front, rear, left, right, etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of description of the present application and to simplify the description, and do not indicate or imply that the apparatus or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present application.
In the description of the present application, a number means one or more, a number means two or more, and greater than, less than, exceeding, etc. are understood to not include the present number, and above, below, within, etc. are understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
Furthermore, in the description of the present application, unless otherwise indicated, "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
In the description of the present application, unless explicitly defined otherwise, terms such as arrangement, installation, connection, etc. should be construed broadly and the specific meaning of the terms in the present application can be reasonably determined by a person skilled in the art in combination with the specific contents of the technical scheme.
In terms of automatic optimization algorithms, there have been many achievements and application practices applied in practical engineering research. From the early genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm and other intelligent optimization algorithms to the current new generation heuristic algorithm, the method plays a great role in solving the practical problems in industry. The introduction of the intelligent algorithm improves the working efficiency and simultaneously liberates the cost and experience of labor. Therefore, the automatic optimization design method using the intelligent algorithm in the design of the acoustic wave filter in the radio frequency front end improves the efficiency and the accuracy of the design, and reduces the cost of labor and the risk of errors.
Based on this, as shown in fig. 1, the present embodiment provides a hybrid acoustic wave filter optimization method for performing topology design and optimization on a hybrid acoustic wave filter, including the following steps:
a1, determining target performance parameters of the filter according to the design indexes.
In this embodiment, the target performance includes one or more of passband bandwidth, insertion loss, out-of-band rejection, in-band ripple, return loss, in-band standing wave ratio, etc.
A2, presetting the topological structure and branch number of the filter.
The topology of the filter can calculate the ABCD matrix of the filter.
The number of branches of the filter is determined according to the difficulty of target performance, and the larger the target performance difficulty is, the more branches are needed.
As an alternative embodiment, the filter branch includes, but is not limited to, one, more or all of a resonator, a capacitor, an inductance, a series or parallel combination of a resonator and a capacitor, and a series or parallel combination of a capacitor and an inductance.
A3, setting a variable individual, a population number, a maximum iteration number and a parameter optimization range of the filter;
and the number of the optimized groups and the maximum iteration number are determined according to the selected optimization algorithm. And the parameter optimization range is determined according to the actual process condition.
A4, initializing a random population.
Specifically, the filter structure is randomly initialized according to the determined population number and randomly assigned within a determined range.
And A5, evaluating the fitness.
Calculating the distance between the performance parameter of each individual topological structure in the generated group and the target performance parameter;
a6, calculating the crowding degree and sequencing the crowding degree and the non-dominance.
Specifically, the crowding degree is calculated according to the distance between each individual and the target performance, and non-dominant individuals are searched for and layered and ordered.
And A7, judging whether the termination condition is met.
Specifically, the termination condition refers to reaching a target performance or reaching a maximum number of iterations.
A8, outputting a filter structure if the termination condition is met; otherwise, updating the group by using an optimization algorithm; and returns to step A5.
If the termination condition is met, outputting a filter structure unit which is most in line with the target performance index, and outputting a filter topology unit with better performance in the optimization process. If the termination condition is not met, the optimization algorithm is used for group updating, wherein the optimization algorithm has multiple choices, and in the embodiment, a moth group flame algorithm is used.
The embodiment also provides an optimization method of the acoustic wave filter with known topological structure and fixed partial parameters, which is used for fine optimization of the hybrid acoustic wave filter so as to achieve target performance. The optimizing method is different from the optimizing step of the mixed acoustic wave filter in that:
a3 is executed after A1, and A2 is not needed to be executed.
A3, determining a filter structure and known parameters, setting a variable individual of the filter, optimizing the population number, the maximum iteration number and the parameter optimization range. Specifically, according to the known filter structure and other parameters, a specific filter variable unit is constructed to reduce the consumption and calculation amount of the storage space to the greatest extent.
The hybrid acoustic wave filter optimization method provided by the embodiment can realize the recombination of various branch structures and the synchronous optimization of the structures and parameters, but because the optimization range is wider, the speed of searching for the optimal solution is slower, and the synchronous optimization may miss some potential high-quality structures.
The optimization method of the acoustic wave filter with the known topological structure and the fixed part parameters provided by the embodiment can finely optimize the parameters of the filter within the specified limit range so as to obtain the element parameter closest to the target performance.
The embodiment also provides an implementation mode, and the two optimization methods are combined for use, and the specific optimization steps are as follows:
a1, reducing target performance requirements.
A8, obtaining a preliminary optimized individual result.
The filter structure or fixed part parameters are determined and execution returns to A3. The optimization method of the acoustic wave filter with known topological structure and fixed partial parameters is used for carrying out fine optimization on the optimized individual result, so that a better individual is found.
The above method is explained in detail below with reference to the drawings and specific examples.
Example 1
A trapezoid acoustic wave filter has a center frequency of 2GHz and a target performance of 1.97GHz<f<At 2.03GHz, |S 21 |<1dB；f<1.9GHz or f>At 2.1GHz, |S 21 |>30dB. Acoustic wave resonator electromechanical coupling coefficient for constructing filter7% and a quality factor (Q) of 1000.
The ladder-shaped acoustic wave filter is directly realized according to engineering experience, the length of a selected filter is 7, the structure is a traditional ladder-shaped filter structure, and only one resonator is arranged in each of a series branch and a parallel branch. The series resonance point of the series-branch resonator is f ss = 2.009GHz, static capacitance C 0s Series resonance point of = 1.089pF and parallel resonator is f sp =1.950 GHz, static capacitance C 0p = 2.009pF. The filter effect is achieved as shown by the dotted line in fig. 3.
The filter effect is achieved by fixing the filter length constant with the series resonance point of the acoustic resonator and adjusting the static capacitance value of the resonator using an automatic optimization tool inside the ADS, as shown by the dashed curve in fig. 3.
When the acoustic wave filter with known topology and fixed partial parameters is optimized, a defined parameter structure is shown in fig. 2. The filter parameter structure comprises a structure and a numerical value, wherein the structure is a fixed value, and the numerical value is a variable. In the optimization process, only the variables that need to be optimized are updated. As in the present embodiment, the filter length is fixed, the series resonance point of the filter branch structure and the acoustic wave resonator is unchanged, and the magnitude of the static capacitance of each resonator is optimized in a reasonable range. The final optimization result is shown in the solid curve in fig. 3.
Fig. 3 shows three structural S parameter comparison graphs obtained by the optimization method proposed by the present application and the non-optimization of the ladder acoustic wave filter and the automatic optimization of the ADS software. From the figure, it can be seen that the result of automatic optimization of the ADS software is improved to some extent in-band compared with the acoustic wave filter built directly from technical experience. According to the optimization method provided by the application, the final optimization result is obtained through 100 iterations. The convergence curve of the optimization parameter increasing with the iteration number is shown in fig. 4, and because the convergence curve is multi-objective optimization of two fitness functions, two fitness values are jointly considered in the optimization process, and therefore the fitness cannot be monotonically decreased in the iteration process. The comparison of the optimization results in fig. 3 shows that the optimization method provided by the application has great advantages compared with the automatic optimization result of the ADS software. Under the condition that the number of the optimized parameters is only 7, the convergence speed is high, and the best result can be obtained within 50 iterations. In summary, the optimization method for the acoustic wave filter aiming at the known topological structure and the fixed part parameters can be applied to practical cases to assist technicians to finely optimize the parameters of the acoustic wave filter and quickly obtain satisfactory results.
Example 2
Mixed broadband acoustic wave filter based on trapezium, with center frequency of 3.5GHz and target performance of 3.2GHz<f<At 3.8GHz, |S 21 |<1dB；f<2.9GHz or f>At 4.1GHz, |S 21 |>25dB. Acoustic wave resonator electromechanical coupling coefficient for constructing filter10% and a quality factor (Q) of 1000.
Because the acoustic resonator is not easy to realize a broadband filter, the filter needs a large amount of debugging through engineering experience to build, and ADS software cannot realize automatic design and optimization on the structure. The optimization algorithm of the application is used for realizing the index of the filter and optimizing all parameters of the filter. If the parameter structure is used for optimization, the optimization process is divided into two steps, after the filter group is initialized at random, the numerical parameters are optimized first, and then the structural parameters are optimized, so that the method is reasonable, but a large amount of calculation space and time are consumed in the actual process. To simplify this optimization process, the parameters of the filter are optimized using a parameter array, which is defined as shown in fig. 5. The filter parameter array is formed by sequentially arranging branches, and each branch comprises a structure and a numerical value which are all variables. The selectable structures of each branch include five possibilities of resonator, capacitor, inductor, resonator in series with inductor and inductor in series with capacitor, as shown in fig. 6, the probability of occurrence can be adjusted according to practical requirements. The five structures are selected for ordering optimization in order to improve the optimization efficiency, but at the same time, reduce the ability of finding the best results. The option of adding a tributary structure is a good approach where computational effort is allowed to be more difficult than the target performance. Considering that in practical situations, too many choices of series resonant frequencies of resonators increase processing difficulty, at most two choices of series resonant frequencies of acoustic wave filters can be defined in the program. The method of optimizing using the parameter array may miss hiding the appropriate solution, but the simultaneous optimization of the structure and the value can greatly reduce the computation space and time cost. Aiming at proper solutions which may be missed, the optimization target can be reduced in a set program, the structure can be fixed when a better structure is found, and then a local optimization method is adopted to carry out fine optimization on the number to obtain the optimization result of the final filter.
According to the above method, the convergence curve of the first optimization parameter with the increase of the iteration number is shown in fig. 11, and is iterated 500 times, the structure 1 is generated by 150 times of iteration (shown in fig. 7), and the structure 2 is iterated 300 times (shown in fig. 9). Fixing two structures to finely optimize element parameters, and respectively displaying S parameter curve results of primary optimization and secondary optimization of the structures 1 and 2 in fig. 8 and 10. The optimization process can be seen that the combined optimization method can quickly find out a potential filter structure capable of realizing target performance, and the numerical value fine optimization process in the secondary structure digs out the result closest to the target performance under the structure. In the first optimization process, besides the final structure, the structure in the optimization process is also worth focusing, and engineers can obtain design elicitations from other high-quality structures selected in the program process.
Example 3
Mixed broadband filter based on trapezium, center frequency of 6.5GHz and target performance of 6GHz<f<At 7.1GHz, |S 21 |<1dB；f<At 5.9GHz, |S 21 |>40dB. Acoustic wave resonator electromechanical coupling coefficient for constructing filter10% and a quality factor (Q) of 1000.
This embodiment is also a wideband filter that requires high out-of-band rejection requirements at low frequencies and stringent roll-off requirements at the lower sidebands. The topology is a ladder filter. Each branch selection includes five cases as shown in fig. 6. Since only the lower sideband has the requirement of high roll-off, there is no requirement on the upper sideband, and only one series resonant frequency of the acoustic wave filter is defined. The first optimization definition is shown in fig. 5, the parameter array optimizes the parameters, and the more suitable filter structure is shown in fig. 12. Secondary optimization definition the parameter structure shown in fig. 2 performs a fine optimization of the parameters of each element in the filter. The S-parameter curves obtained by the two optimizations are shown in fig. 13. The optimization process parameter optimization convergence curve is shown in fig. 14, and the iteration number is 750.
The embodiment verifies the feasibility of the combined optimization method again, can be applied to a conventional band-pass filter, can also be used for targeted optimization of a filter with an irregular special index, shows irreplaceable adaptability, and provides a new thought for the design of a hybrid acoustic wave filter. Plays a great role in reducing the capacity requirements of engineers and improving the design efficiency.
In the embodiment of the application, the branch structure uses ideal capacitance and inductance elements, the resonator uses an MBVD model for simulation, and in practical application, the model which is more in line with the real elements can be replaced to obtain more accurate results, and correspondingly, the numerical value part of each individual parameter array or parameter structure body increases the occupied space along with the increase of the complexity of the optimized model.
In some alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present application are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed, and in which sub-operations described as part of a larger operation are performed independently.
Furthermore, unless indicated to the contrary, one or more of the functions and/or features described may be integrated in a single physical device and/or software module or one or more functions and/or features may be implemented in separate physical devices or software modules. It should be understood that a detailed discussion of the actual implementation of each module is not necessary to an understanding of the present application. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be apparent to those skilled in the art from consideration of their attributes, functions and internal relationships. Accordingly, one of ordinary skill in the art can implement the application as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative and are not intended to be limiting upon the scope of the application, which is to be defined in the appended claims and their full scope of equivalents.
In the foregoing description of the present specification, reference has been made to the terms "one embodiment/example", "another embodiment/example", "certain embodiments/examples", and the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the application, the scope of which is defined by the claims and their equivalents.
While the preferred embodiment of the present application has been described in detail, the present application is not limited to the above embodiments, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the present application, and these equivalent modifications and substitutions are intended to be included in the scope of the present application as defined in the appended claims.
Claims (10)
1. A method of optimizing a hybrid acoustic wave filter, comprising the steps of:
s1, determining target performance parameters of a filter according to design indexes;
s2, presetting the branch number of a filter;
s3, setting variable individuals, population numbers, maximum iteration times and parameter optimization ranges of the filters;
s4, initializing a group;
s5, calculating the distance between each topological structure and the target performance, and obtaining fitness evaluation;
s6, calculating the crowding degree and performing non-dominant sorting according to the obtained distance between each topological structure and the target performance;
s7, judging whether the optimal individual meets the termination condition;
s8, if the filter structure is met, outputting the filter structure; otherwise, the optimization algorithm updates the population and returns to step S5.
2. The method for optimizing a hybrid acoustic wave filter according to claim 1, wherein the method is applicable to the structural optimization of all filters for which ABCD matrices can be calculated.
3. The method of claim 1, wherein the target performance comprises at least one of passband bandwidth, insertion loss, out-of-band rejection, in-band ripple, return loss, or in-band standing wave ratio.
4. The method of claim 1, wherein the number of branches of the filter is determined based on the difficulty of the target performance, and the greater the difficulty of the target performance, the greater the number of branches required.
5. The method of claim 1, wherein the branches of the filter comprise one, more than one, or all of resonators, capacitors, inductors, a combination of resonators and inductors in series or parallel, a combination of resonators and capacitors in series or parallel, and a combination of capacitors and inductors in series or parallel.
6. The method of optimizing a hybrid acoustic wave filter according to claim 1, wherein the individual variables of the filter are defined as a parameter structure and a parameter array; defining a variable individual by using a parameter structure body under the condition that the branch structure is fixed; the parameter array is used to define the variable individuals in the case of an unfixed branch structure.
7. The method of claim 1, wherein the number of optimized groups and the maximum number of iterations are determined according to a selected optimization algorithm; and the parameter optimization range is determined according to the actual process condition.
8. The method of optimizing a hybrid acoustic wave filter according to claim 1, wherein in step S8, the step of updating the population comprises:
and updating the population by adopting a moth group flame algorithm.
9. The method of optimizing a hybrid acoustic wave filter according to claim 1, wherein for the case where the filter structure is known, step S1 is followed directly by step S3 without performing S2; the step S3 comprises the following steps:
based on the known filter structure and part of the structural parameters, the variable individuals of the filter are constructed to achieve further optimization of the hybrid acoustic wave filter.
10. The method of optimizing a hybrid acoustic wave filter of claim 1, further comprising:
taking the filter structure output in the step S8 as a known filter structure, constructing a variable unit of the filter according to the known filter structure, and returning to the step S3.
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| US20190312570A1 (en) * | 2016-12-23 | 2019-10-10 | Huawei Technologies Co., Ltd. | Filter and method for adjusting performance of filter |
| CN114492263A (en) * | 2022-02-17 | 2022-05-13 | 广东广纳芯科技有限公司 | Filter design system, method and computer readable medium |
| CN116451633A (en) * | 2023-03-29 | 2023-07-18 | 电子科技大学 | Adaptive reflectionless filter design method based on hybrid genetic particle swarm optimization |
| CN116579222A (en) * | 2023-07-12 | 2023-08-11 | 深圳飞骧科技股份有限公司 | Optimization method, system and related equipment for parameters of surface acoustic wave filter |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US20190312570A1 (en) * | 2016-12-23 | 2019-10-10 | Huawei Technologies Co., Ltd. | Filter and method for adjusting performance of filter |
| CN114492263A (en) * | 2022-02-17 | 2022-05-13 | 广东广纳芯科技有限公司 | Filter design system, method and computer readable medium |
| CN116451633A (en) * | 2023-03-29 | 2023-07-18 | 电子科技大学 | Adaptive reflectionless filter design method based on hybrid genetic particle swarm optimization |
| CN116579222A (en) * | 2023-07-12 | 2023-08-11 | 深圳飞骧科技股份有限公司 | Optimization method, system and related equipment for parameters of surface acoustic wave filter |
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