Design method of multi-stage adjustable MEMS sensor micro-signal amplification circuit

Abstract

The invention provides a design method of a multi-stage adjustable MEMS sensor micro-signal amplification circuit, and belongs to the technical field of circuit design. The method comprises the following steps: acquiring a design parameter range of the MEMS sensor for signal amplification circuit design, and acquiring to-be-amplified micro signal feature information; randomly generating a first design parameter in the design parameter range, and obtaining a multi-stage amplification parameter and a plurality of electronic noise parameters under the first design parameter; carrying out amplification applicability analysis to obtain amplification applicability parameters; and carrying out amplification quality analysis to obtain a plurality of amplification quality parameters, calculating to obtain a design score in combination with the amplification applicability parameters, and optimizing the design parameters to obtain an optimal design parameter as a design result. Optimization design is carried out by comprehensively considering the micro-signal feature information and the electronic noise parameters, and the amplification effect and applicability of the micro-signal amplification circuit of the MEMS sensor are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of circuit design, and in particular to a design method for a multi-stage adjustable MEMS sensor micro-signal amplification circuit. Background Art

[0002] MEMS (micro-electromechanical system) sensors, as miniature sensing devices, are widely used in vibration detection, pressure measurement, acceleration sensing, and other fields. Because the signals output by MEMS sensors are typically weak, they need to be amplified by an amplifier circuit to a signal strength suitable for subsequent processing.

[0003] Currently, the design of micro-signal amplification circuits for MEMS sensors primarily utilizes a multi-stage amplification structure composed of operational amplifiers. However, existing amplification circuit designs often employ fixed amplification factors, making it difficult to adapt to input signals of varying amplitudes. When the input signal amplitude varies significantly, under- or over-amplification is prone to occur, resulting in unsatisfactory amplification effects. Furthermore, existing technologies typically focus only on the selection of amplification factors when designing amplification circuits. During the multi-stage amplification process, the cumulative effect of electronic noise introduced by the amplification circuit itself significantly reduces the signal-to-noise ratio, affecting the amplification quality. Furthermore, existing design methods often rely on experience, resulting in insufficient applicability of the designed amplification circuits. Summary of the Invention

[0004] The present invention aims to solve the technical problems of unsatisfactory amplification effect and insufficient applicability in the design of MEMS sensor micro-signal amplifier circuits in the prior art, and provides a design method for a multi-stage adjustable MEMS sensor micro-signal amplifier circuit to solve the problems.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] The present invention provides a design method for a multi-stage adjustable MEMS sensor micro-signal amplification circuit, comprising: obtaining a design parameter range for designing a signal amplification circuit of a MEMS sensor, and obtaining characteristic information of a micro-signal to be amplified, wherein the micro-signal characteristic information includes a micro-signal amplitude distribution and a micro-signal noise distribution; randomly generating a first design parameter within the design parameter range, and obtaining multi-stage amplification parameters and multiple electronic noise parameters under the first design parameter; performing an amplification suitability analysis based on the micro-signal amplitude distribution and the multi-stage amplification parameters to obtain amplification suitability parameters; performing an amplification quality analysis based on the micro-signal noise distribution and the multiple electronic noise parameters to obtain multiple amplification quality parameters; combining the amplification suitability parameters, calculating a design score, and optimizing the design parameters to obtain optimal design parameters as a design result.

[0007] Optionally, obtaining a design parameter range for a signal amplification circuit design of a MEMS sensor and obtaining characteristic information of a micro-signal to be amplified includes: obtaining a design parameter range for a signal amplification circuit design of a MEMS sensor; obtaining characteristic information of a micro-signal to be amplified, wherein the micro-signal characteristic information includes a micro-signal amplitude distribution and a micro-signal noise distribution of multiple sample micro-signals, and each micro-signal noise includes a signal noise density.

[0008] Optionally, randomly generating a first design parameter within the design parameter range, and obtaining multi-stage amplification parameters and multiple electronic noise parameters under the first design parameter, includes: randomly generating a first design parameter within the design parameter range; obtaining a multi-stage amplification factor under the first design parameter; performing electronic noise simulation prediction based on the first design parameter and the multi-stage amplification factor, and obtaining multiple electronic noise parameters under the multi-stage amplification factor.

[0009] Optionally, electronic noise simulation prediction is performed based on the first design parameters and multi-stage amplification factors to obtain multiple electronic noise parameters under the multi-stage amplification factors, including: obtaining an electronic noise simulator; combining the first design parameters with the multi-stage amplification factors respectively and inputting them into the electronic noise simulator, and outputting the multiple electronic noise parameters under the multi-stage amplification factors.

[0010] Optionally, the training steps of the electronic noise simulator include: collecting a sample design parameter set and a sample amplification factor set based on historical test data of the MEMS sensor amplifier circuit, and collecting the electronic noise density obtained by testing under different sample design parameters and sample amplification factors, and marking the obtained sample electronic noise parameter set; using the design parameters and amplification factors as input data and the electronic noise parameters as output data to construct the structure of the electronic noise simulator; using the sample design parameter set, sample amplification factor set and sample electronic noise parameter set to perform supervised training on the electronic noise simulator until convergence, to obtain a trained electronic noise simulator.

[0011] Optionally, an amplification suitability analysis is performed based on the WeChat signal amplitude distribution and the multi-stage amplification parameters to obtain the amplification suitability parameters, including: performing amplification suitability combinations and counting based on the WeChat signal amplitude distribution and the multi-stage amplification parameters to obtain a total count of multiple amplification parameters of the multi-stage amplification parameters; calculating discrete parameters of the total counts of the multiple amplification parameters, and obtaining the amplification suitability parameters based on the discrete parameter calculations.

[0012] Optionally, based on the micro-signal amplitude distribution and the multi-stage amplification parameters, applicable amplification combinations and counting are performed to obtain the total count of multiple amplification parameters of the multi-stage amplification parameters, including: randomly combining the micro-signal amplitude within the micro-signal amplitude distribution with the amplification parameters within the multi-stage amplification parameters, and judging whether the micro-signal amplitude will be amplified to the target signal strength; if so, using the corresponding amplification parameter as the adaptive amplification parameter, and counting the adaptive amplification parameter; if not, not counting until the combination counting is completed, and obtaining multiple adaptive amplification parameters and the total count of multiple amplification parameters.

[0013] Optionally, an amplification quality analysis is performed based on the micro-signal noise distribution and multiple electronic noise parameters to obtain multiple amplification quality parameters, including: combining multiple micro-signal noises within the micro-signal noise distribution with electronic noise parameters corresponding to multiple adapted amplification parameters to calculate multiple total amplification noise parameters; calculating multiple amplification signal-to-noise ratios based on the multiple total amplification noise parameters; and calculating multiple amplification quality parameters based on the multiple amplification signal-to-noise ratios.

[0014] Optionally, in combination with the amplification applicability parameter, a design score is calculated, and the design parameters are optimized to obtain optimal design parameters as design results, including: calculating a design score based on the amplification applicability parameter and multiple amplification quality parameters; and iteratively optimizing the design parameters according to the design score to obtain optimal design parameters as design results.

[0015] Optionally, according to the design score, iterative optimization of design parameters is performed to obtain optimal design parameters with the maximum design score as the design result, including: continuing to randomly generate design parameters and calculating the design score; performing iterative optimization of design parameters for a convergence number of times to obtain optimal design parameters with the maximum design score as the design result.

[0016] The beneficial effects of the present invention are:

[0017] The design parameter range for the MEMS sensor signal amplification circuit design is obtained, along with characteristic information of the micro-signal to be amplified, including the micro-signal amplitude distribution and micro-signal noise distribution. By obtaining the design parameter range, the constraints and adjustable range of the amplification circuit design are determined. By obtaining the micro-signal characteristic information, the basic characteristics of the signal to be processed are clarified, providing a targeted basis for subsequent amplification circuit design. A first design parameter is randomly generated within the design parameter range, and multi-stage amplification parameters and multiple electronic noise parameters under the first design parameter are obtained. The randomly generated first design parameter provides a starting point for the optimization process. By obtaining the multi-stage amplification parameters, the specific configurations of different amplification levels are determined. By obtaining the multiple electronic noise parameters, the noise characteristics introduced by the amplification circuit during operation are predicted. Based on the micro-signal amplitude distribution and the multi-stage amplification parameters, an amplification suitability analysis is performed to obtain the amplification suitability parameters. Through the amplification suitability analysis, the processing capabilities of the multi-stage amplification parameters for micro-signals of varying amplitudes are evaluated, ensuring uniformity in the use of each amplification level, thereby improving the circuit's suitability for different signal amplitudes. Based on the micro-signal noise distribution and multiple electronic noise parameters, an amplification quality analysis is performed to obtain multiple amplification quality parameters. Combined with the amplification suitability parameters, a design score is calculated and optimized to obtain the optimal design parameters as the design result. Through amplification quality analysis, quality indicators such as the signal-to-noise ratio after amplification are evaluated; the design score is calculated based on the amplification suitability parameters, comprehensively considering both suitability and quality. Through design parameter optimization, the optimal design parameters are found within the design parameter range, thereby obtaining design parameters that both improve amplification effect and enhance suitability.

[0018] Through the above technical solution, based on the characteristic information of the micro-signal, the amplification applicability and amplification quality are comprehensively considered, and the optimal design parameters are obtained through optimization design, thereby effectively solving the technical problems of unsatisfactory amplification effect and insufficient applicability in the design of the micro-signal amplification circuit of the MEMS sensor in the existing technology, and achieving the technical effect of improving the amplification effect and applicability of the micro-signal amplification circuit of the MEMS sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic flow chart of a design method for a multi-stage adjustable MEMS sensor micro-signal amplification circuit provided by the present invention;

[0020] Figure 2 This is a schematic diagram of the process of training an electronic noise simulator provided by the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0023] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed herein.

[0024] Example 1, as Figure 1 As shown, an embodiment of the present invention provides a design method for a multi-stage adjustable MEMS sensor micro-signal amplification circuit, including:

[0025] S1. Obtain a design parameter range for a MEMS sensor signal amplification circuit design, and obtain characteristic information of a micro-signal to be amplified, wherein the micro-signal characteristic information includes a micro-signal amplitude distribution and a micro-signal noise distribution.

[0026] Specifically, first, the design parameter range for the MEMS sensor signal amplification circuit design is obtained. This design parameter range includes, but is not limited to, the value boundaries of key circuit design parameters such as the amplifier's gain range, bandwidth range, power consumption range, input impedance range, output impedance range, and the range of the number of stages for multi-stage amplification. These parameter ranges are determined based on the MEMS sensor's technical specifications, application scenario requirements, and the physical constraints of the circuit implementation.

[0027] At the same time, characteristic information of the micro-signal to be amplified is obtained. Micro-signal characteristic information refers to the statistical characteristics of the weak signal output by the MEMS sensor in the actual working environment, specifically including the micro-signal amplitude distribution and the micro-signal noise distribution. Among them, the micro-signal amplitude distribution refers to the probability distribution characteristics of the micro-signal amplitude obtained by performing historical data statistical analysis on the micro-signal in the target application environment. For example, for vibration sensor applications, a large amount of sample data is collected under different vibration intensity environments, and the distribution law of the micro-signal amplitude is statistically obtained, which reflects the dynamic range characteristics of the signal to be amplified. The micro-signal noise distribution refers to the statistical distribution characteristics of the inherent noise components in the micro-signal, including parameters such as signal-noise density. The micro-signal noise distribution is obtained by performing spectral analysis or time-domain statistical analysis on the micro-signal, reflecting the signal-to-noise ratio level and noise characteristics of the original signal, providing a reference basis for the subsequent amplification circuit design.

[0028] By obtaining the above-mentioned design parameter range and WeChat signal characteristic information, the foundation is laid for the subsequent optimization design of the multi-stage adjustable amplifier circuit, ensuring that the design scheme can be optimized for specific WeChat signal characteristics within a reasonable parameter constraint range.

[0029] S2. Randomly generate a first design parameter within the design parameter range, and obtain a multi-stage amplification parameter and a plurality of electronic noise parameters under the first design parameter.

[0030] Specifically, based on the obtained design parameter range, a first design parameter is randomly generated within the design parameter range. The random generation process adopts a pseudo-random number generation algorithm to randomly generate a set of specific design parameters as an initial design scheme. The design parameters include, but are not limited to, circuit parameters such as the bias voltage, load resistance, feedback resistance, coupling capacitance, and op amp model selection of each level of amplifier. In view of the design characteristics of the micro-signal amplification circuit of the MEMS sensor, different types of design parameters are sampled using corresponding probability distribution functions. Specifically, for continuous design parameters such as bias voltage, resistance value, and capacitance value, uniform distribution is used for random sampling within their engineering achievable range; for discrete design parameters such as op amp model and circuit topology, discrete uniform distribution is used for random selection.

[0031] Determining the first design parameter by random generation can avoid the bias problem that may be caused by traditional design parameter selection based on experience or fixed rules, provide a starting point for the subsequent iterative optimization process, facilitate the optimization process to conduct a global search within the entire design parameter range, and increase the possibility of ultimately obtaining the globally optimal design parameters.

[0032] Subsequently, based on the first design parameter, a multi-stage amplification parameter is further obtained. The multi-stage amplification parameter refers to the multiple different amplification factors that the amplifier circuit can provide under the current first design parameter configuration, such as discrete amplification factors such as ×10, ×100, and ×1000. The multi-stage amplification parameter can achieve adaptive amplification of micro signals of different amplitudes, where a large amplification factor is used to process small amplitude signals and a small amplification factor is used to process large amplitude signals, thereby amplifying input signals of various amplitudes to a target intensity range that is convenient for subsequent circuit processing and monitoring.

[0033] At the same time, multiple electronic noise parameters are obtained under the first design parameters. Electronic noise parameters refer to various noise components introduced by the amplifier circuit during the signal amplification process, primarily including thermal noise, shot noise, flicker noise, and electromagnetic interference noise. The generation of these electronic noises is closely related to the specific design parameters of the amplifier circuit, including factors such as component selection, circuit topology, and operating point setting.

[0034] By obtaining the multi-stage amplification parameters and multiple electronic noise parameters under the first design parameters, a complete performance characteristic description of the first design parameters was established. The multi-stage amplification parameters determined the circuit's processing capability for micro-signals of different amplitudes, while the electronic noise parameters quantified the noise level introduced during the amplification process, providing a basis for subsequent amplification applicability analysis and amplification quality analysis, thereby conducting a comprehensive evaluation of the first design parameters and guiding the subsequent optimization iterative process.

[0035] S3. Perform an amplification suitability analysis based on the WeChat signal amplitude distribution and the multi-stage amplification parameters to obtain the amplification suitability parameters.

[0036] Specifically, based on the acquired WeChat signal amplitude distribution and the acquired multi-stage amplification parameters, an amplification suitability analysis is performed to evaluate the coverage capability and processing effect of the current first design parameters on WeChat signals of different amplitudes.

[0037] Amplification suitability analysis combines the individual WeChat signal amplitude values ​​in the WeChat signal amplitude distribution with the various amplification factors in the multi-stage amplification parameters to determine whether each WeChat signal amplitude can be amplified to the preset target signal strength using the corresponding amplification factor. The target signal strength is the ideal signal amplitude range for subsequent circuit processing and signal monitoring.

[0038] During the combination matching process, for each WeChat signal amplitude in the WeChat signal amplitude distribution, all amplification factors in the multi-stage amplification parameters are traversed, the amplified signal strength is calculated, and a determination is made as to whether it falls within the target signal strength. If a certain amplification factor factor successfully amplifies the WeChat signal amplitude to the target range, the amplification factor factor is marked as the adapted amplification parameter for the WeChat signal, and the number of times the amplification factor factor is used is counted and accumulated.

[0039] By traversing and analyzing all WeChat signal amplitudes in the WeChat signal amplitude distribution, the total count of each amplification factor level in the multi-stage amplification parameter is obtained, i.e., the total amplification parameter count. The total amplification parameter count reflects the frequency of use of each amplification factor level when processing the WeChat signal amplitude distribution.

[0040] Then, based on the total counts of multiple amplification parameters, their statistical discrete parameters, such as the variance, are calculated to measure the uniformity of the number of times each amplification factor level is used. The smaller the discrete parameter, the more uniform the number of times each amplification factor level is used. In other words, the multi-level amplification parameter can evenly process WeChat signals of different amplitudes, avoiding idleness of certain amplification factors and improving the efficiency of circuit resources.

[0041] Afterwards, the amplification suitability parameter is obtained based on the discrete parameter calculation. For example, the inverse of the discrete parameter is used as the amplification suitability parameter, so that the value of the amplification suitability parameter is positively correlated with the suitability of the multi-level amplification parameter, providing a quantitative indicator for subsequent comprehensive evaluation.

[0042] S4. Perform an amplification quality analysis based on the micro-signal noise distribution and multiple electronic noise parameters to obtain multiple amplification quality parameters. Combined with the amplification suitability parameters, a design score is calculated and a design parameter is optimized to obtain the optimal design parameters as a design result.

[0043] Specifically, first, the micro-signal noise in the micro-signal noise distribution is combined with the obtained multiple electronic noise parameters for calculation. Specifically, for each micro-signal noise in the micro-signal noise distribution, the electronic noise parameter corresponding to each amplification factor in the multi-stage amplification parameter is superimposed and calculated to obtain multiple total amplification noise parameters. The total amplification noise parameter comprehensively considers the noise characteristics of the micro-signal itself and the noise influence introduced by the amplifier circuit at each amplification factor. Subsequently, based on the amplified signal intensity and the corresponding total amplification noise parameter, multiple amplification signal-to-noise ratios are calculated. The amplification signal-to-noise ratio reflects the signal quality level of the micro-signal after processing with different amplification factors, and evaluates the performance of the amplifier circuit from the perspective of signal quality.

[0044] Subsequently, multiple amplification quality parameters are calculated based on the multiple amplification signal-to-noise ratios. For example, the amplification quality parameters are calculated by subtracting the signal-to-noise ratio from 1, resulting in a negative correlation between the values ​​of the amplification quality parameters and the actual amplification quality, facilitating subsequent optimization calculations. Next, a weighted calculation method is used to calculate a design score based on the obtained amplification suitability parameter and the multiple amplification quality parameters. The design score comprehensively reflects the overall performance of the current first design parameter in terms of signal coverage capability and amplification quality. This dual-dimensional evaluation allows for a comprehensive assessment of the pros and cons of the first design parameter.

[0045] Afterwards, the design parameters are iteratively optimized according to the design score to obtain the optimal design parameters with the maximum design score. Specifically, new design parameters are continuously randomly generated within the design parameter range, and the corresponding design scores are calculated according to the above-mentioned process S2-S4. After multiple iterative comparisons, the design parameter configuration with the highest design score is retained. After iterative optimization for a preset number of convergence times, the optimal design parameters with the maximum design score are obtained as the final design result. The optimal design parameters achieve the best balance in terms of amplification applicability and amplification quality, and can effectively solve the problems of unsatisfactory amplification effect and insufficient applicability in the design of MEMS sensor micro-signal amplification circuits in the prior art, and realize high-quality amplification processing of micro-signals with different characteristics.

[0046] Furthermore, the design parameter range of the MEMS sensor for signal amplification circuit design is obtained, and characteristic information of the micro signal to be amplified is obtained, including:

[0047] S11. Obtaining a design parameter range for a MEMS sensor signal amplification circuit design;

[0048] S12. Acquire characteristic information of the WeChat signal to be amplified, wherein the WeChat signal characteristic information includes WeChat signal amplitude distribution and WeChat signal noise distribution of multiple sample WeChat signals, and each WeChat signal noise includes signal-to-noise density.

[0049] In one feasible implementation, first, the design parameter range for the signal amplification circuit design is determined based on the technical specifications of the MEMS sensor, the application scenario requirements, and the engineering implementation constraints of the amplification circuit. The design parameter range covers the parameter boundaries in the amplification circuit design, including but not limited to the bias voltage range of each amplifier stage, the load resistance value range, the feedback resistance value range, the coupling capacitor capacitance range, the op amp model selection range, and the circuit topology selection range. The determination of the design parameter range is based on the output characteristics of the MEMS sensor, the performance requirements of the target application, and the technical indicators and cost constraints of the circuit components. By clarifying the value boundaries of the design parameters, clear constraints are provided for subsequent random parameter generation and optimization search, ensuring that the generated design solution is feasible in engineering implementation.

[0050] Then, by collecting and analyzing actual WeChat signal samples in the target application environment, the characteristic information of the WeChat signal to be amplified is obtained. The WeChat signal characteristic information is obtained through statistical analysis of multiple sample WeChat signals, specifically including WeChat signal amplitude distribution and WeChat signal noise distribution. Among them, WeChat signal amplitude distribution refers to the probability distribution characteristics of WeChat signal amplitude obtained by statistical analysis of the amplitude values ​​of multiple sample WeChat signals; WeChat signal amplitude distribution reflects the variation law and dynamic range characteristics of WeChat signal amplitude in the target application scenario, and provides a reference basis for the subsequent multi-stage amplification parameter design. WeChat signal noise distribution refers to the statistical distribution characteristics of noise components in multiple sample WeChat signals. Each WeChat signal noise includes signal noise density, which characterizes the noise power level within the unit frequency band; by analyzing the noise characteristics of multiple sample WeChat signals, the WeChat signal noise distribution is obtained, which provides the original noise characteristic data for the subsequent amplification quality analysis and signal-to-noise ratio calculation.

[0051] By obtaining WeChat signal characteristic information, it is ensured that the amplification circuit design scheme can be optimized according to the specific signal characteristics, thereby improving the applicability and effectiveness of the design parameters.

[0052] Furthermore, randomly generating a first design parameter within the design parameter range, and obtaining a multi-stage amplification parameter and a plurality of electronic noise parameters under the first design parameter, including:

[0053] S21, randomly generating a first design parameter within the design parameter range;

[0054] S22, obtaining a multi-stage magnification under the first design parameters;

[0055] S23. Perform electronic noise simulation prediction based on the first design parameters and the multi-stage amplification factor to obtain multiple electronic noise parameters under the multi-stage amplification factor.

[0056] In a preferred embodiment, first, a pseudo-random number generation algorithm is used to randomly generate first design parameters based on a determined design parameter range. The random generation process employs corresponding probability distribution functions for different design parameter types. For continuous design parameters such as bias voltage, resistor value, and capacitor value, a uniform distribution is used for random sampling within their engineeringly achievable range. For discrete design parameters such as op amp model and circuit topology, a discrete uniform distribution is used for random selection. Obtaining the first design parameters through random generation provides a concrete circuit configuration foundation for subsequent performance analysis and evaluation.

[0057] Then, based on the circuit configuration information in the first design parameter, a multi-stage amplification factor is obtained through circuit analysis and calculation. The multi-stage amplification factor refers to a plurality of different amplification factors that can be achieved by the amplifier circuit under the current first design parameter configuration. Specifically, based on the gain settings of each amplifier, the feedback resistor configuration, and the cascade connection mode in the first design parameter, the single-stage gain of each amplifier is calculated through circuit theory analysis, and then the amplification factor options that the entire multi-stage amplifier circuit can provide are determined, such as discrete amplification factors such as ×10, ×100, and ×1000. These multi-stage amplification factors provide optional amplification schemes for subsequent signal adaptation analysis.

[0058] Subsequently, electronic noise simulation prediction is performed based on the first design parameter and the obtained multi-stage amplification factor to obtain multiple electronic noise parameters at each amplification factor level. The electronic noise simulation prediction process is based on a circuit noise theoretical model and comprehensively considers the impact of factors such as component selection, circuit topology, and operating point setting in the first design parameter on electronic noise. By performing noise prediction on the first design parameter and the corresponding multi-stage amplification factor, various electronic noise parameters such as thermal noise, shot noise, and flicker noise introduced by the amplifier circuit at different amplification factors are calculated. Multiple electronic noise parameters at each amplification factor level are obtained, quantifying the noise contribution of the amplifier circuit during the signal amplification process and providing noise characteristic data for subsequent amplification quality analysis and signal-to-noise ratio calculation.

[0059] Furthermore, electronic noise simulation prediction is performed based on the first design parameters and the multi-stage amplification factor to obtain multiple electronic noise parameters under the multi-stage amplification factor, including:

[0060] S231, obtain an electronic noise simulator;

[0061] S232 , combining the first design parameters with multiple levels of amplification factors and inputting them into an electronic noise simulator, and outputting a plurality of electronic noise parameters under the multiple levels of amplification factors.

[0062] In a preferred embodiment, first, a pre-trained electronic noise simulator is obtained. The electronic noise simulator is a noise prediction model constructed based on a machine learning algorithm. It can accurately predict the electronic noise parameters generated by the amplifier circuit under corresponding operating conditions based on the input design parameters and amplification factor configuration. The electronic noise simulator is trained and learned from a large amount of historical test data of MEMS sensor amplifier circuits to establish a mapping relationship between design parameters, amplification factors, and electronic noise parameters. The electronic noise simulator can quickly and accurately predict the noise characteristics under different circuit configurations, avoiding the time cost and resource consumption of a large number of actual tests required in traditional methods.

[0063] Subsequently, the first design parameters are combined with the multiple amplification factors to form multiple parameter combinations, which are then input into the electronic noise simulator for noise prediction. Specifically, for each amplification factor level in the multi-stage amplification factor, it is paired and combined with the first design parameter to form multiple parameter combinations. Each parameter combination contains circuit design parameters and corresponding amplification factors, providing a prediction basis for the electronic noise simulator. These parameter combinations are sequentially input into the electronic noise simulator, which outputs electronic noise parameters corresponding to each amplification factor level. Electronic noise parameters include quantized values ​​of various noise components such as thermal noise density, shot noise density, and flicker noise density.

[0064] Through batch prediction, the electronic noise characteristic data of the first design parameter at all multi-level amplification factors can be efficiently obtained, providing basic noise information for subsequent amplification quality analysis.

[0065] Further, such as Figure 2 As shown, the training steps of the electronic noise simulator include:

[0066] S2311. Based on historical test data of the MEMS sensor amplifier circuit, collect a sample design parameter set and a sample amplification factor set, collect the electronic noise density obtained by testing under different sample design parameters and sample amplification factors, and annotate the obtained sample electronic noise parameter set;

[0067] S2312, using the design parameters and the amplification factor as input data and the electronic noise parameters as output data, to construct a structure of an electronic noise simulator;

[0068] S2313. Perform supervised training on the electronic noise simulator using the sample design parameter set, the sample magnification set, and the sample electronic noise parameter set until convergence, thereby obtaining a trained electronic noise simulator.

[0069] In a preferred embodiment, first, based on the historical test data of the MEMS sensor amplifier circuit, a training sample data set is collected, including a sample design parameter set and a sample amplification factor set. Specifically, a sample design parameter set is extracted from the historical test data, and the sample design parameter set includes the actual values ​​of design parameters such as bias voltage, load resistance, feedback resistance, coupling capacitance, and op amp model under different circuit configurations. At the same time, a sample amplification factor set corresponding to the sample design parameters is collected, and the amplification factor level setting used in the actual test of each circuit configuration is recorded. Furthermore, the electronic noise density obtained through actual circuit testing under different sample design parameter and sample amplification factor combinations is collected. The electronic noise density includes the measured values ​​of various noise components such as thermal noise density, shot noise density, and flicker noise density. By sorting and annotating these measured noise data, a sample electronic noise parameter set is obtained, and a corresponding relationship data set between input parameters and output noise parameters is formed.

[0070] Next, the network structure and data processing framework of the electronic noise simulator are constructed. Design parameters and amplification factors are used as input data for the electronic noise simulator, while electronic noise parameters are used as output data. An input-output mapping relationship is established. The electronic noise simulator can be constructed using machine learning algorithms such as neural networks, support vector machines, and random forests. Taking a neural network as an example, a multilayer perceptron (MLP) structure is used to construct the electronic noise simulator. Specifically, the neural network consists of an input layer, multiple hidden layers, and an output layer. The input layer contains n neurons, which receive a feature vector consisting of design parameters and amplification factors, such as bias voltage, load resistance, feedback resistance, and amplification factor. The first hidden layer contains 128 neurons and uses the ReLU activation function for nonlinear transformation. The second hidden layer contains 64 neurons, also using the ReLU activation function. The output layer contains m neurons, corresponding to m different types of electronic noise parameters, such as thermal noise density, shot noise density, and flicker noise density. Through a forward propagation process, the neural network processes the input design parameters and amplification factor feature vectors through nonlinear transformations in each hidden layer, ultimately generating the corresponding electronic noise parameter prediction results in the output layer. The number of layers and neurons in the network structure can be adjusted and optimized according to the complexity of the training data and the prediction accuracy requirements.

[0071] Next, supervised training of the electronic noise simulator is performed using a set of sample design parameters, a set of sample amplification factors, and a set of sample electronic noise parameters as training data. During training, the sample design parameters and sample amplification factors are used as input, and the corresponding sample electronic noise parameters are used as the desired output. The internal parameters of the electronic noise simulator are adjusted using a backpropagation algorithm or other optimization algorithm to minimize the error between the simulator's predicted output and the actual electronic noise parameters. The training process continues until the simulator's prediction performance converges, meaning that the improvement in the prediction error over multiple consecutive iterations is less than a preset threshold, or until a preset maximum number of training cycles is reached. Upon convergence, a fully trained electronic noise simulator is obtained, capable of accurately predicting electronic noise parameters based on the input design parameters and amplification factors.

[0072] Furthermore, an amplification suitability analysis is performed based on the WeChat signal amplitude distribution and the multi-stage amplification parameters to obtain the amplification suitability parameters, including:

[0073] S31. Perform amplification and counting based on the WeChat signal amplitude distribution and the multi-stage amplification parameters to obtain a total count of multiple amplification parameters of the multi-stage amplification parameters;

[0074] S32. Calculate a discrete parameter of the total count of the multiple amplification parameters, and obtain an amplification suitability parameter based on the discrete parameter calculation.

[0075] In a preferred embodiment, first, based on the WeChat signal amplitude distribution and the multi-stage amplification parameters, an amplification applicable combination analysis and a usage frequency count are performed to evaluate the adaptability of each amplification factor gear to WeChat signals of different amplitudes. Specifically, for each WeChat signal amplitude in the WeChat signal amplitude distribution, a combination calculation is performed with each amplification factor gear in the multi-stage amplification parameter in turn to determine whether the WeChat signal amplitude can reach the preset target signal strength after being amplified by a specific amplification factor. If a certain amplification factor can successfully amplify the WeChat signal amplitude to within the target range, the amplification factor is marked as the adaptive amplification parameter of the WeChat signal. By traversing all WeChat signal amplitudes in the WeChat signal amplitude distribution, the number of times each amplification factor gear is selected as the adaptive amplification parameter is statistically counted, and finally the total amplification parameter count of each amplification factor gear in the multi-stage amplification parameter is obtained. The total amplification parameter count reflects the frequency distribution of use of each amplification factor gear when processing the WeChat signal amplitude distribution.

[0076] Then, based on the total counts of the multiple amplification parameters obtained, its discrete parameters are calculated, and the amplification suitability parameters are further calculated based on the discrete parameters. Specifically, the variance of the total counts of the multiple amplification parameters is calculated as a discrete parameter, and the variance reflects the degree of dispersion of the number of times each amplification factor gear is used. The smaller the discrete parameter, the closer the number of times each amplification factor gear is used is to a uniform distribution, that is, the multi-stage amplification parameters can be used in a balanced manner to process micro signals of different amplitudes, avoiding some amplification factors from being idle, and improving the utilization efficiency of circuit resources. Subsequently, the inverse of the discrete parameter is used as the amplification suitability parameter, so that the amplification suitability parameter is positively correlated with the actual suitability of the multi-stage amplification parameter. When the number of times each amplification factor gear is used is more uniform, the smaller the discrete parameter, and the larger the corresponding amplification suitability parameter, indicating that the current design parameters have better signal coverage capability and resource utilization.

[0077] Furthermore, according to the WeChat signal amplitude distribution and the multi-stage amplification parameters, amplification applicable combinations and counting are performed to obtain a total count of multiple amplification parameters of the multi-stage amplification parameters, including:

[0078] S311, randomly combining the WeChat signal amplitude within the WeChat signal amplitude distribution with the amplification parameters within the multi-stage amplification parameters, and determining whether the WeChat signal amplitude will be amplified to the target signal strength;

[0079] S312: If yes, use the corresponding amplification parameter as the adaptive amplification parameter and count the adaptive amplification parameter; if no, do not count until the combined counting is completed to obtain multiple adaptive amplification parameters and a total count of the multiple amplification parameters.

[0080] In a preferred embodiment, first, each micro-signal amplitude in the micro-signal amplitude distribution is combined one by one with each amplification parameter in the multi-stage amplification parameter, and the applicability of each combination is judged. Specifically, for each micro-signal amplitude in the micro-signal amplitude distribution, it is paired and combined with each amplification factor gear in the multi-stage amplification parameter in turn, and the signal strength of the micro-signal amplitude after amplification by the corresponding amplification factor is calculated. Then, it is judged whether the amplified signal strength falls within the preset target signal strength. The target signal strength is an ideal signal amplitude interval pre-set according to the requirements of subsequent circuit processing and signal monitoring. Through a systematic combination traversal and judgment process, the matching relationship between each micro-signal amplitude and each amplification factor gear can be comprehensively evaluated, providing complete basic data for subsequent adaptability analysis.

[0081] If a combination of a WeChat signal amplitude and a specific amplification factor successfully amplifies the WeChat signal amplitude to the target signal strength, the amplification factor is marked as the WeChat signal's adaptive amplification parameter, and the number of times the adaptive amplification parameter is used is counted. If the target signal strength requirement is not met, the amplification factor is not marked as an adaptive parameter, and the counting operation is not performed accordingly.

[0082] By traversing all WeChat signal amplitudes in the WeChat signal amplitude distribution until all possible combinations are determined and counted, a complete record of multiple adaptive amplification parameters and the total amplification parameter count corresponding to each amplification factor level is obtained. The total amplification parameter count counts the frequency with which each amplification factor level is selected as the adaptation parameter when processing the entire WeChat signal amplitude distribution, providing quantitative statistical basis data for subsequent calculation of amplification suitability parameters.

[0083] Furthermore, an amplification quality analysis is performed based on the micro-signal noise distribution and multiple electronic noise parameters to obtain multiple amplification quality parameters, including:

[0084] S41, combining multiple micro-signal noises within the micro-signal noise distribution with electronic noise parameters corresponding to multiple adaptive amplification parameters to calculate and obtain multiple total amplified noise parameters;

[0085] S42. Calculating and obtaining a plurality of amplified signal-to-noise ratios according to a plurality of total amplified noise parameters;

[0086] S43. Calculate and obtain multiple amplification quality parameters according to the multiple amplification signal-to-noise ratios.

[0087] In a preferred embodiment, first, multiple micro-signal noises within the micro-signal noise distribution are combined with the electronic noise parameters introduced by the corresponding adaptive amplification parameters to obtain multiple total amplified noise parameters. Specifically, for each micro-signal noise in the micro-signal noise distribution, the electronic noise parameter corresponding to the adaptive amplification parameter is obtained according to the determined adaptive amplification parameter. The noise component in the original micro-signal is superimposed with the electronic noise parameter introduced by the amplification circuit at the corresponding amplification factor to calculate the total amplified noise parameter of the micro-signal after amplification. The total amplified noise parameter comprehensively reflects the inherent noise characteristics of the signal itself and the noise contribution generated by the amplification circuit under specific working conditions, providing complete noise characteristic data for subsequent signal quality evaluation.

[0088] Then, based on the obtained multiple total amplified noise parameters and the corresponding amplified signal strength, multiple amplified signal-to-noise ratios are calculated. Specifically, for each amplified micro-signal, the ratio of its amplified signal strength to the corresponding total amplified noise parameter is calculated to obtain the amplified signal-to-noise ratio of the micro-signal. The amplified signal-to-noise ratio quantifies the signal quality level of the micro-signal after multi-stage amplification processing and is an important indicator for evaluating the performance of the amplifier circuit. By calculating the amplified signal-to-noise ratios of all micro-signals in the micro-signal noise distribution, multiple amplified signal-to-noise ratio values ​​are obtained, reflecting the amplification quality performance of the current first design parameter for micro-signals with different noise characteristics.

[0089] Subsequently, multiple amplification quality parameters are calculated based on the obtained multiple amplification signal-to-noise ratios. Specifically, the amplification quality parameters can be calculated by subtracting the amplification signal-to-noise ratio from 1. This calculation method ensures that the amplification quality parameters are negatively correlated with the actual signal quality. That is, the higher the signal-to-noise ratio, the smaller the amplification quality parameters, facilitating subsequent optimization calculations.

[0090] Furthermore, the design score is calculated based on the amplified applicability parameters, and the design parameters are optimized to obtain the optimal design parameters as the design results, including:

[0091] S44. Calculate and obtain a design score based on the amplification applicability parameter and multiple amplification quality parameters;

[0092] S45. Perform iterative optimization of design parameters according to the design score to obtain optimal design parameters as a design result.

[0093] In a preferred embodiment, first, based on the obtained amplification applicability parameter and the obtained multiple amplification quality parameters, a comprehensive evaluation method is used to calculate the design score. The design score comprehensively reflects the comprehensive performance of the current design scheme in amplification applicability and amplification quality, and provides a quantitative evaluation standard for subsequent optimization iterations. Specifically, a weighted calculation method is used to comprehensively evaluate the amplification applicability parameters and the amplification quality parameters. Corresponding weight coefficients α and β are set, where α is the weight coefficient of the amplification applicability parameter and β is the weight coefficient of the amplification quality parameter. The design score is calculated by weighted summation, for example: design score = α × amplification applicability parameter + β × Σ (statistical value of amplification quality parameter). Among them, the statistical value of the amplification quality parameter is calculated using the average value of multiple amplification quality parameters. The setting of the weight coefficients α and β can be adjusted according to the emphasis on signal coverage capability and amplification quality in the specific application scenario. For example, for application scenarios requiring high signal coverage, α can be set to 0.7 and β can be set to 0.3, giving more emphasis to amplification applicability. For precision measurement applications requiring high signal quality, α can be set to 0.4 and β can be set to 0.6, giving more emphasis to amplification quality, so as to balance the contribution ratio of the two evaluation dimensions in the overall score.

[0094] For example, assuming the magnification suitability parameter obtained in a certain iteration is 0.8, and multiple magnification quality parameters are {0.1, 0.15, 0.12, 0.09, 0.13}, the average value is used as the statistical value of the magnification quality parameters, that is, the statistical value is (0.1 + 0.15 + 0.12 + 0.09 + 0.13) / 5 = 0.118. For general application scenarios, set the weight coefficients α = 0.6 and β = 0.4, and the design score is calculated as: Design Score = 0.6 × 0.8 + 0.4 × 0.118 = 0.48 + 0.0472 = 0.5272.

[0095] Afterwards, the design parameters are iteratively optimized according to the design score, and the optimal design parameters are obtained as the final design result through multiple rounds of iterative search. Specifically, an iterative optimization strategy is adopted, and the complete process of steps S21 to S44 is repeated in each iteration: new design parameters are randomly regenerated within the design parameter range, the corresponding multi-stage amplification parameters and electronic noise parameters are obtained, amplification applicability analysis and amplification quality analysis are performed, and a new design score is calculated. By comparing the design score of the newly generated design parameters with the current optimal design score, the design parameter configuration with the better design score is retained. A convergence condition is set, and the iterative optimization process ends when the preset number of iterations is reached or the improvement in the design score is less than the preset threshold.

[0096] After iterative optimization, the design parameter configuration with the best design score was obtained as the optimal design parameter configuration and the final design result. The optimal design parameters achieved the best balance between amplification applicability and amplification quality, effectively solving the technical problems in the design of micro-signal amplification circuits for MEMS sensors.

[0097] Furthermore, according to the design score, iterative optimization of the design parameters is performed to obtain the optimal design parameters with the maximum design score as a design result, including:

[0098] S451. Continue to randomly generate design parameters and calculate design scores;

[0099] S452. Perform iterative optimization of the design parameters for a convergence number of times to obtain the optimal design parameters with the maximum design score as the design result.

[0100] In a preferred embodiment, first, based on the iterative optimization strategy, new design parameters are continuously randomly generated within the design parameter range, and the corresponding design scores are calculated. Specifically, a new set of design parameters is randomly regenerated within the design parameter range. Based on the newly generated design parameters, the steps of multi-stage amplification parameter acquisition, electronic noise parameter prediction, amplification applicability analysis, amplification quality analysis, etc. are sequentially performed, and finally the design scores of the newly generated design parameters are obtained by a weighted calculation method. By repeatedly executing the random generation and scoring calculation process, more design parameter combinations can be explored within the design parameter range, increasing the possibility of finding the global optimal solution. Each new design parameter generated is independent of the previous design parameters, ensuring the randomness and diversity of the search process.

[0101] The design parameters are iteratively optimized by setting the number of convergences, ultimately obtaining the optimal design parameters with the maximum design score as the design result. During each iteration, the newly calculated design score is compared with the currently saved optimal design score. If the new design score is greater than the current optimal design score, the optimal design score is updated and the corresponding design parameters are saved as the current optimal design parameters. If the new design score is not better than the current optimal value, the current optimal design parameters remain unchanged.

[0102] A preset number of convergences is set as the iteration termination condition. When the number of iterations reaches the convergence number, the optimization process ends. The convergence number can be set based on factors such as design accuracy requirements, computing resource limitations, and time constraints. After sufficient iterations for the convergence number, the design parameter configuration with the maximum design score is obtained as the optimal design parameters and the final design result. The optimal design parameters represent the circuit design solution that can achieve the best balance between amplification applicability and amplification quality under given constraints, effectively solving the design optimization problem of MEMS sensor micro-signal amplification circuits.

[0103] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0104] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0105] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0106] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0108] Although preferred embodiments of the present invention have been described, additional changes and modifications to these embodiments may occur to those skilled in the art once the basic inventive concepts become known.

[0109] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A design method for a multi-stage adjustable MEMS sensor micro-signal amplification circuit, characterized in that: The method comprises: Obtaining a design parameter range for a MEMS sensor signal amplification circuit design, and obtaining characteristic information of a micro-signal to be amplified, wherein the micro-signal characteristic information includes a micro-signal amplitude distribution and a micro-signal noise distribution; Randomly generating a first design parameter within the design parameter range, and obtaining a multi-stage amplification parameter and a plurality of electronic noise parameters under the first design parameter; Performing an amplification suitability analysis based on the WeChat signal amplitude distribution and the multi-stage amplification parameters to obtain the amplification suitability parameters; Based on the micro-signal noise distribution and multiple electronic noise parameters, an amplification quality analysis is performed to obtain multiple amplification quality parameters. Combined with the amplification suitability parameters, a design score is calculated and obtained, and the design parameters are optimized to obtain the optimal design parameters as the design results.

2. The design method of the multi-stage adjustable MEMS sensor micro-signal amplification circuit according to claim 1 is characterized in that: Obtain the design parameter range for the MEMS sensor signal amplification circuit design and obtain the characteristic information of the micro signal to be amplified, including: Obtain the design parameter range for MEMS sensor signal amplification circuit design; Acquire WeChat signal feature information to be amplified, wherein the WeChat signal feature information includes WeChat signal amplitude distribution and WeChat signal noise distribution of multiple sample WeChat signals, and each WeChat signal noise includes signal-to-noise density.

3. The design method of the multi-stage adjustable MEMS sensor micro-signal amplification circuit according to claim 1 is characterized in that: Randomly generating a first design parameter within the design parameter range, and obtaining a multi-stage amplification parameter and a plurality of electronic noise parameters under the first design parameter, including: randomly generating a first design parameter within the design parameter range; Obtaining multi-stage magnification under the first design parameters; According to the first design parameters and the multi-stage amplification factor, electronic noise simulation prediction is performed to obtain multiple electronic noise parameters under the multi-stage amplification factor.

4. The design method of the multi-stage adjustable MEMS sensor micro-signal amplification circuit according to claim 3 is characterized in that: Based on the first design parameters and the multi-stage amplification factor, an electronic noise simulation prediction is performed to obtain multiple electronic noise parameters under the multi-stage amplification factor, including: Get an electronic noise simulator; The first design parameters are combined with multiple levels of magnification to input into an electronic noise simulator, and multiple electronic noise parameters under the multiple levels of magnification are obtained as output.

5. The design method of the multi-stage adjustable MEMS sensor micro-signal amplification circuit according to claim 4 is characterized in that: The training steps of the electronic noise simulator include: Based on the historical test data of the MEMS sensor amplifier circuit, a sample design parameter set and a sample amplification factor set are collected. The electronic noise density obtained by testing under different sample design parameters and sample amplification factors is collected, and the sample electronic noise parameter set is marked. The structure of the electronic noise simulator is constructed by taking the design parameters and the amplification factor as input data and the electronic noise parameters as output data; The electronic noise simulator is supervised trained using the sample design parameter set, the sample magnification set, and the sample electronic noise parameter set until convergence, thereby obtaining a trained electronic noise simulator.

6. The design method of the multi-stage adjustable MEMS sensor micro-signal amplification circuit according to claim 1, characterized in that: According to the WeChat signal amplitude distribution and the multi-stage amplification parameters, an amplification suitability analysis is performed to obtain the amplification suitability parameters, including: According to the WeChat signal amplitude distribution and the multi-stage amplification parameters, amplification applicable combinations and counting are performed to obtain a total count of multiple amplification parameters of the multi-stage amplification parameters; A discrete parameter of the total count of the plurality of amplification parameters is calculated, and an amplification suitability parameter is obtained according to the discrete parameter calculation.

7. The design method of the multi-stage adjustable MEMS sensor micro-signal amplification circuit according to claim 6, characterized in that: According to the WeChat signal amplitude distribution and the multi-stage amplification parameters, amplification applicable combinations and counting are performed to obtain a total count of multiple amplification parameters of the multi-stage amplification parameters, including: Randomly combining the WeChat signal amplitude within the WeChat signal amplitude distribution with the amplification parameters within the multi-stage amplification parameters to determine whether the WeChat signal amplitude will be amplified to the target signal strength; If yes, the corresponding amplification parameter is used as the adaptive amplification parameter and the adaptive amplification parameter is counted. If no, no counting is performed until the combined counting is completed to obtain multiple adaptive amplification parameters and a total count of multiple amplification parameters.

8. The design method of the multi-stage adjustable MEMS sensor micro-signal amplification circuit according to claim 7, characterized in that: An amplification quality analysis is performed based on the micro-signal noise distribution and multiple electronic noise parameters to obtain multiple amplification quality parameters, including: Calculate and obtain a plurality of total amplified noise parameters by combining the plurality of micro-signal noises within the micro-signal noise distribution with the electronic noise parameters corresponding to the plurality of adapted amplification parameters; Calculating and obtaining multiple amplified signal-to-noise ratios according to multiple total amplified noise parameters; A plurality of amplification quality parameters are obtained by calculation according to the plurality of amplification signal-to-noise ratios.

9. The design method of the multi-stage adjustable MEMS sensor micro-signal amplification circuit according to claim 1, characterized in that: In combination with the amplification applicability parameters, a design score is calculated and the design parameters are optimized to obtain the optimal design parameters as a design result, including: Calculating a design score based on the scale-up suitability parameter and multiple scale-up quality parameters; According to the design score, the design parameters are iteratively optimized to obtain the optimal design parameters as the design result.

10. The design method of the multi-stage adjustable MEMS sensor micro-signal amplification circuit according to claim 9, characterized in that: According to the design score, iterative optimization of the design parameters is performed to obtain the optimal design parameters with the maximum design score as a design result, including: Continue to randomly generate design parameters and calculate design scores; The design parameters are iteratively optimized for the number of convergence times to obtain the optimal design parameters with the maximum design score as the design result.

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