Low noise amplifier architecture design method and device, storage medium and electronic equipment
By integrating the nonlinear dynamics, noise stochastic processes, and circuit stability of low-noise amplifiers through a total system analytical mechanical model and optimization algorithm, a multi-objective cost function is constructed, which solves the problem of low efficiency in low-noise amplifier design and realizes automated collaborative optimization and global optimal design.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGXI XINBAITE MICROELECTRONICS CO LTD
- Filing Date
- 2026-02-11
- Publication Date
- 2026-06-02
Smart Images
Figure CN122133474A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of amplifier technology, specifically to a low-noise amplifier architecture design method, apparatus, storage medium, and electronic device. Background Technology
[0002] Low-noise amplifiers (LNAs) are key modules in the RF receiver front-end, and their performance directly determines the sensitivity, dynamic range, and reliability of the entire communication or sensing system. Amplifier design is essentially a complex multi-objective optimization problem seeking the optimal trade-off between several conflicting performance metrics. Core design objectives typically include: achieving the lowest possible noise figure (NF) to minimize signal loss, maintaining high linearity (e.g., a high third-order intermodulation cutoff point IIP3) to suppress interference, and ensuring absolute stability under various operating conditions.
[0003] Currently, the design of low-noise amplifiers generally adopts a step-by-step, sequential design paradigm, treating noise optimization, stability design, and linearity correction as independent sub-problems. This paradigm is based on separate mathematical models (such as linear noise theory, small-signal S-parameter models, and large-signal nonlinear models) and is completed in different electronic design automation (EDA) tool modules.
[0004] However, current low-noise amplifier designs rely heavily on engineers' experience, involving manual iteration and trial and error, and making empirical trade-offs among multiple performance metrics, resulting in low design efficiency. Summary of the Invention
[0005] This application provides a method, apparatus, storage medium, and electronic device for designing a low-noise amplifier architecture, which can improve the design efficiency of low-noise amplifier architectures.
[0006] In a first aspect, embodiments of this application provide a low-noise amplifier architecture design method, including: Generate a total system analytical mechanical model describing the architecture of the target low-noise amplifier, wherein the total system analytical mechanical model integrates a first sub-model describing the nonlinear dynamics of the amplifier, a second sub-model describing the random noise process, and a third sub-model characterizing the stability constraints of the circuit. Based on the analytical mechanical model of the overall system, a multi-objective cost function containing multiple amplifier performance indicators is constructed. The multi-objective cost function is iteratively optimized using an optimization algorithm to obtain the target amplifier design parameters; Output the target amplifier design parameters and construct the target low-noise amplifier architecture based on the target amplifier design parameters.
[0007] In the low-noise amplifier architecture design method provided in this application embodiment, the step of generating a total system analytical mechanical model describing the target low-noise amplifier architecture includes: A first sub-model describing the nonlinear dynamics of the amplifier, a second sub-model describing the random noise process, and a third sub-model characterizing the stability constraints of the circuit are generated respectively. The first sub-model, the second sub-model, and the third sub-model are mathematically coupled to form an analytical mechanical model of the overall system.
[0008] In the low-noise amplifier architecture design method provided in this application embodiment, a first sub-model describing the amplifier's nonlinear dynamics is generated, including: The nonlinear current-voltage characteristics and nonlinear capacitance characteristics of transistors in amplifier circuits are mapped to nonlinear potential energy functions or nonlinear kinetic energy functions under the analytical mechanical framework. Based on the nonlinear potential energy function or nonlinear kinetic energy function, a nonlinear Hamiltonian or nonlinear Lagrangian describing the nonlinear dynamics of the amplifier is constructed to form the first sub-model.
[0009] In the low-noise amplifier architecture design method provided in this application embodiment, generating a second sub-model describing the random noise process includes: The thermal noise source in the amplifier circuit is modeled as a Gaussian white noise random force coupled to the system's generalized velocity or generalized momentum; The random fluctuation energy corresponding to the random force of Gaussian white noise is introduced into the Hamiltonian or Lagrangian of the analytical mechanics framework to construct the noise Hamiltonian or noise Lagrangian that describes the random process of noise, thus forming the second sub-model.
[0010] In the low-noise amplifier architecture design method provided in this application embodiment, a third sub-model characterizing circuit stability constraints is generated, including: Based on the first sub-model and the second sub-model, the system matrix of the overall system analytical mechanical model is derived after linearization near the static operating point; The Nyquist stability criterion or pole location constraint is transformed into a mathematical inequality constraint on the real part of the eigenvalues of the system matrix, thus forming a third sub-model.
[0011] In the low-noise amplifier architecture design method provided in this application embodiment, the step of iteratively optimizing the multi-objective cost function through an optimization algorithm to obtain the target amplifier design parameters includes: Based on the overall system analytical mechanical model, the gradient information of the multi-objective cost function with respect to the current amplifier design parameters is calculated; Based on the gradient information and the preset step size, the current amplifier design parameters are updated within the feasible region that satisfies the circuit stability constraints. Repeat the gradient calculation and parameter update steps above until the preset convergence condition is met, and output the target amplifier design parameters that meet all performance requirements.
[0012] In the low-noise amplifier architecture design method provided in this application embodiment, the preset convergence condition includes at least one of the following: The value of the multi-objective cost function changes less than a first threshold in multiple consecutive iterations; The amount of update to the amplifier design parameters in multiple consecutive iterations is less than the second threshold. The number of iterations has reached the preset maximum number of iterations.
[0013] Secondly, embodiments of this application provide a low-noise amplifier architecture design apparatus, comprising: The model generation unit is used to generate a total system analytical mechanical model describing the target low-noise amplifier architecture. The total system analytical mechanical model integrates a first sub-model describing the amplifier's nonlinear dynamics, a second sub-model describing the noise stochastic process, and a third sub-model characterizing the circuit stability constraints. The function construction unit is used to construct a multi-objective cost function containing multiple amplifier performance indicators based on the analytical mechanical model of the overall system. The iterative optimization unit is used to iteratively optimize the multi-objective cost function through an optimization algorithm to obtain the target amplifier design parameters; The parameter output unit is used to output the target amplifier design parameters and construct the target low-noise amplifier architecture based on the target amplifier design parameters.
[0014] Thirdly, this application provides a storage medium storing a plurality of instructions adapted for loading by a processor to execute the low-noise amplifier architecture design method described in any of the preceding claims.
[0015] Fourthly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the low-noise amplifier architecture design method described in any of the preceding claims.
[0016] In summary, the low-noise amplifier architecture design method provided in this application includes generating a total system analytical mechanical model describing the target low-noise amplifier architecture. This total system analytical mechanical model integrates a first sub-model describing the amplifier's nonlinear dynamics, a second sub-model describing the noise stochastic process, and a third sub-model characterizing circuit stability constraints. Based on the total system analytical mechanical model, a multi-objective cost function containing multiple amplifier performance indicators is constructed. The multi-objective cost function is iteratively optimized using an optimization algorithm to obtain target amplifier design parameters. The target amplifier design parameters are output, and the target low-noise amplifier architecture is constructed based on these parameters. This application embodiment can improve the design efficiency of low-noise amplifier architectures. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a schematic diagram illustrating an application scenario of the low-noise amplifier architecture design method provided in this application embodiment.
[0019] Figure 2 This is a flowchart illustrating the low-noise amplifier architecture design method provided in the embodiments of this application.
[0020] Figure 3 This is a schematic diagram of the low-noise amplifier architecture design device provided in the embodiments of this application.
[0021] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation
[0022] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.
[0023] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.
[0024] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.
[0025] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.
[0026] In the description of this application, it should be noted that the terms "upper," "lower," "left," "right," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. In addition, terms such as "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.
[0027] Currently, the design of low-noise amplifiers generally adopts a step-by-step, sequential design paradigm, treating noise optimization, stability design, and linearity correction as independent sub-problems. This paradigm is based on separate mathematical models (such as linear noise theory, small-signal S-parameter models, and large-signal nonlinear models) and is completed in different electronic design automation (EDA) tool modules.
[0028] However, current low-noise amplifier designs neglect the inherent physical coupling and interaction between noise, nonlinearity, and stability. This can lead to the implicit deterioration of other performance metrics when optimizing local performance, making it difficult to achieve global optimum. Furthermore, the trade-off process relying on manual intervention is inefficient, yields suboptimal results, and fails to systematically explore the design space. Additionally, the computational tools and model frameworks supporting each design stage are fragmented, lacking a unified and coherent collaborative optimization capability, severely hindering the automated design and innovation of low-noise amplifiers.
[0029] Based on this, embodiments of this application provide a low-noise amplifier architecture design method, apparatus, storage medium, and electronic device. Specifically, the low-noise amplifier architecture design apparatus can be integrated into an electronic device, which can be a server or a terminal, etc. The terminal can include mobile phones, wearable smart devices, tablets, laptops, and personal computers (PCs), etc., and other computer and auxiliary devices. The server can be a single server or a server cluster composed of multiple servers, and can be a physical server or a virtual server.
[0030] For example, such as Figure 1 As shown, the electronic device can generate a total system analytical mechanical model describing the architecture of the target low-noise amplifier. The total system analytical mechanical model integrates a first sub-model describing the nonlinear dynamics of the amplifier, a second sub-model describing the random noise process, and a third sub-model characterizing the stability constraints of the circuit. Based on the total system analytical mechanical model, a multi-objective cost function containing multiple amplifier performance indicators is constructed. The multi-objective cost function is iteratively optimized using an optimization algorithm to obtain the design parameters of the target amplifier. The design parameters of the target amplifier are output, and the architecture of the target low-noise amplifier is constructed based on the design parameters of the target amplifier.
[0031] The technical solutions shown in this application will be described in detail below through specific embodiments. It should be noted that the order of description of the following embodiments is not intended to limit the priority of the embodiments.
[0032] Please see Figure 2 , Figure 2 This is a flowchart illustrating the low-noise amplifier architecture design method provided in this application embodiment. The specific flow of the low-noise amplifier architecture design method can be as follows: 101. Generate a total system analytical mechanical model describing the architecture of the target low-noise amplifier. The total system analytical mechanical model integrates a first sub-model describing the nonlinear dynamics of the amplifier, a second sub-model describing the random noise process, and a third sub-model characterizing the stability constraints of the circuit.
[0033] In this embodiment, the overall system analytical mechanics model integrates a first sub-model describing the nonlinear dynamics of the amplifier, a second sub-model describing the random noise process, and a third sub-model characterizing the circuit stability constraints.
[0034] Before constructing the analytical mechanical model of the overall system, the implementation topology of the amplifier circuit can be obtained by selecting from a known library of typical low-noise amplifier topologies or by drawing a basic circuit connection diagram based on design specifications.
[0035] Subsequently, the nonlinear current-voltage and nonlinear capacitance characteristics of transistors in the amplifier circuit, as well as the thermal noise generated by passive components such as resistors (whose characteristics are described by a Gaussian white noise model), are used as the physical objects of description. Secondly, based on the mapping relationship between circuit variables and generalized coordinates / momentum in analytical mechanics, and the principle of stochastic mechanics that equates random noise to random forces, a framework is provided for the mathematical expression of the model. Finally, based on linearizing the nonlinear system at the static operating point and transforming circuit stability criteria (such as the Nyquist criterion) into eigenvalue constraints of the linear system matrix, a basis is provided for stability modeling.
[0036] In the specific implementation process, the generation of the overall system analytical mechanical model includes the following steps: First, a first sub-model describing the nonlinear dynamics of the amplifier, a second sub-model describing the random noise process, and a third sub-model characterizing the stability constraints of the circuit are generated.
[0037] For the first sub-model, the generation process includes: mapping the nonlinear current-voltage characteristics and nonlinear capacitance characteristics of the transistors in the amplifier circuit to a nonlinear potential energy function or a nonlinear kinetic energy function under an analytical mechanics framework; and constructing a nonlinear Hamiltonian H describing the nonlinear dynamics of the amplifier based on this nonlinear potential energy function or nonlinear kinetic energy function. amp This can be achieved by using nonlinear Lagrangian quantities, thus forming the first sub-model. The core of this first sub-model lies in transforming the nonlinear differential equations of the circuit into canonical equations or Lagrangian equations within the framework of analytic mechanics.
[0038] For the second sub-model, the generation process includes: modeling the thermal noise source in the amplifier circuit as a Gaussian white noise random force coupled with the system's generalized velocity or generalized momentum; and introducing the random fluctuation energy corresponding to this Gaussian white noise random force into the Hamiltonian or Lagrangian of the analytical mechanics framework to construct the noise Hamiltonian H describing the random noise process. noise Or noise Lagrangian quantities, thus forming a second sub-model. This process incorporates stochastic processes such as thermal noise and shot noise into a unified deterministic energy framework through stochastic terms.
[0039] The generation process of the third sub-model includes: based on the first and second sub-models, deriving the system matrix after linearization of the overall system's analytical mechanical model near the static operating point; and transforming the Nyquist stability criterion or pole location constraints into mathematical inequality constraints on the real parts of the system matrix's eigenvalues, thus forming the third sub-model. This process transforms engineering stability requirements into specific mathematical constraints on the stability of the system's energy function or equations of motion.
[0040] Subsequently, the first, second, and third sub-models are mathematically coupled to form the overall analytical mechanical model of the system. This coupling process can be completed within a Hamiltonian or Lagrange mechanical framework. Within the Hamiltonian framework, the nonlinear Hamiltonian (H...) is used... amp ), noise Hamiltonian (corresponding to H) noise ) and terms or conditions that embody stability constraints (H) stability By combining these methods, the total Hamiltonian H can be constructed. total Its expression is H total =H amp +H noise +H stability Among them, H stability Essentially, this represents the constraints on the total energy or dynamics of the system that must be satisfied to ensure circuit stability. Within the framework of Lagrangian mechanics, the total Lagrangian quantity and its equations of motion are constructed by synthesizing nonlinear Lagrangian quantities and noise Lagrangian quantities, and by introducing generalized forces or constraint terms corresponding to stability constraints into the equations of motion. This constitutes a complete mathematical model that uniformly describes the amplifier's nonlinear behavior, noise processes, and stability boundaries.
[0041] 102. Based on the analytical mechanical model of the overall system, a multi-objective cost function containing multiple amplifier performance indicators is constructed.
[0042] In this embodiment, a total system analytical mechanics model can be used to systematically extract or calculate multiple performance indicators related to the core performance of the target low-noise amplifier architecture, and a multi-objective cost function can be constructed based on these performance indicators. The construction process of this multi-objective cost function can be as follows: ①Performance index extraction and quantification: Based on the first sub-model in the overall system analytical mechanics model, the linearity index of the quantizer, such as the input third-order intermodulation cutoff point (IIP3), can be obtained by performing harmonic balance analysis or large-signal simulation on the system's equations of motion.
[0043] Based on the second sub-model in the overall system analytical mechanics model, the noise figure (NF) can be quantified by calculating the contribution of the noise Hamiltonian or noise Lagrange to the fluctuations in the system output state.
[0044] The overall energy representation based on the analytical mechanical model of the total system can directly or indirectly evaluate the average power consumption under certain input signal conditions.
[0045] Based on the third sub-model in the overall system analytical mechanics model, the stability margin (such as gain margin and phase margin) can be quantified by analyzing the eigenvalues of the linearized system matrix or by applying the Nyquist criterion.
[0046] ② Construction of the cost function term: For each of the above performance metrics, a corresponding cost function term can be constructed. Each cost function term maps the actual (or predicted) value of the performance metric to a scalar cost.
[0047] For example, it can be defined as: F NF (X): The noise figure cost term for the design parameter set X, whose value increases with the increase of the noise figure.
[0048] F IIP3 (X): The linearity cost term with respect to the design parameter set X, whose value increases as IIP3 decreases (linearity deteriorates).
[0049] F Power (X): Power consumption cost term with respect to design parameter set X.
[0050] F Stability (X): The stability cost term with respect to the design parameter set X, whose value increases as the stability margin decreases.
[0051] ③ Formation of the multi-objective cost function: By combining the aforementioned competing cost function terms, we can obtain the multi-objective cost function J(X). For example, a weighted summation method can be used to combine multiple cost function terms, as follows: J(X) = w1·F NF (X)+w2·F IIP3 (X)+w3·F Power (X)+w4·F Stability (X); Here, w1, w2, w3, and w4 are weighting coefficients, corresponding to the relative importance of noise performance, linearity, power consumption, and stability, respectively. These weighting coefficients can be adjusted according to the design priorities of specific application scenarios. For example, for extremely high-sensitivity receivers, a higher weight can be assigned to the noise figure (w1 being larger); for high-linearity applications, a higher weight can be assigned to IIP3 (w2 being larger).
[0052] The value of this multi-objective cost function J(X) comprehensively reflects the overall deviation of the amplifier's key performance characteristics from the ideal target under given design parameters X. In subsequent optimization steps, the target amplifier design parameters are the set of parameters X that minimizes (or falls below a certain threshold) the value of this multi-objective cost function J(X) while satisfying all constraints. * Therefore, the multi-objective cost function J(X) explicitly defines the mathematical optimization objective of the entire automated design process: finding the optimal parameter X. * , so that J(X) *) minimized.
[0053] This embodiment allows multiple discrete performance indices derived from a unified physical model to be integrated into a structured, computable optimization objective, thereby transforming the complex, multi-constrained amplifier design problem into a well-defined mathematical optimization problem.
[0054] 103. The multi-objective cost function is iteratively optimized using an optimization algorithm to obtain the design parameters of the target amplifier.
[0055] In this embodiment, the multi-objective cost function J(X) is used as the optimization objective, and the amplifier design parameters (including but not limited to transistor size, bias point, matching network element values and feedback network values) are used as optimization variables. Iterative optimization calculations are performed within the feasible region defined by the circuit stability constraints defined by the third sub-model and other physical realizable constraints to obtain the target amplifier design parameters.
[0056] In practice, a set of current amplifier design parameters can be initialized first, serving as the starting point for iterative optimization. Then, an iterative loop is entered. In each iteration, based on the overall system analytical mechanical model, the gradient information of the multi-objective cost function with respect to the current amplifier design parameters is calculated. This gradient calculation fully utilizes the analytical properties of the overall system analytical mechanical model and can be efficiently solved using the adjoint variable method or automatic differentiation techniques.
[0057] Next, based on the gradient information and a preset step size, a gradient-based optimization algorithm (such as gradient descent or quasi-Newton methods) is used to update the current amplifier design parameters within the feasible region that satisfies circuit stability constraints. During the parameter update process, numerical methods such as the penalty function method, Lagrange multiplier method, or projection method can be used to ensure that the updated amplifier design parameters always satisfy all circuit stability constraints and transistor physical implementation constraints. Then, the above gradient calculation and parameter update steps are repeated until the preset convergence condition is met, and the target amplifier design parameters that meet all performance requirements are output.
[0058] After each iteration, it can be determined whether a preset convergence condition is met. This preset convergence condition includes at least one of the following: the change in the value of the multi-objective cost function in consecutive iterations is less than a first threshold; the update amount of the amplifier design parameters in consecutive iterations is less than a second threshold; and the number of iterations reaches a preset maximum number of iterations.
[0059] Understandably, if the convergence condition is not met, the gradient calculation and parameter update steps described above are repeated based on the updated current amplifier design parameters. Finally, when the convergence condition is met, the iteration terminates, and the current amplifier design parameters at this point are output. These current amplifier design parameters are the target amplifier design parameters.
[0060] 104. Output the target amplifier design parameters and construct the target low-noise amplifier architecture based on the target amplifier design parameters.
[0061] In the actual implementation process, the target amplifier design parameters can be output as a structured design specification document.
[0062] This design specification document contains all the electrical parameters and topology information required to achieve the target low-noise amplifier architecture, including: the precise aspect ratio and bias voltage of the transistors, the component parameters of the inductors and capacitors in the input and output matching networks, and the structure and component values of the feedback network.
[0063] Based on this design specification document, electronic design automation tools can be used to directly generate the corresponding circuit schematics and layouts, or guide manual routing, thereby completing the physical construction, simulation verification and final implementation of the target low-noise amplifier architecture.
[0064] In summary, the low-noise amplifier architecture design method provided in this application includes generating a total system analytical mechanical model describing the target low-noise amplifier architecture. This total system analytical mechanical model integrates a first sub-model describing the amplifier's nonlinear dynamics, a second sub-model describing the noise stochastic process, and a third sub-model characterizing circuit stability constraints. Based on the total system analytical mechanical model, a multi-objective cost function containing multiple amplifier performance indicators is constructed. The multi-objective cost function is iteratively optimized using an optimization algorithm to obtain the target amplifier design parameters. The target amplifier design parameters are output, and the target low-noise amplifier architecture is constructed based on these parameters. This application, by generating a total system analytical mechanical model integrating amplifier nonlinear dynamics, noise stochastic processes, and circuit stability constraints, fundamentally characterizes the inherent physical coupling relationships between various performance indicators, avoiding the implicit performance degradation and lack of global optimality caused by neglecting interactions in traditional discrete designs. Based on the overall system analytical mechanics model, a multi-objective cost function is constructed and iteratively optimized using an optimization algorithm. This achieves automated collaborative optimization and systematic design space exploration for multiple objectives such as noise figure, linearity, and stability, replacing the inefficient and suboptimal trial-and-error trade-off process that relies on manual intervention. In other words, it improves the design efficiency of low-noise amplifier architectures. This application's embodiments unify the traditionally fragmented design processes and computational tools into a coherent analytical mechanics model and optimization flow, significantly enhancing the automation capabilities of low-noise amplifier design.
[0065] To facilitate better implementation of the low-noise amplifier architecture design method provided in this application, this application also provides a low-noise amplifier architecture design apparatus. The meanings of the terms used are the same as in the low-noise amplifier architecture design method described above, and specific implementation details can be found in the descriptions within the method embodiments.
[0066] Please see Figure 3 , Figure 3 This is a schematic diagram of the low-noise amplifier architecture design device provided in an embodiment of this application. The low-noise amplifier architecture design device may include a model generation unit 201, a function construction unit 202, an iterative optimization unit 203, and a parameter output unit 204. The model generation unit 201 is used to generate a total system analytical mechanical model describing the architecture of the target low-noise amplifier. The total system analytical mechanical model integrates a first sub-model describing the nonlinear dynamics of the amplifier, a second sub-model describing the random noise process, and a third sub-model characterizing the stability constraints of the circuit. Function construction unit 202 is used to construct a multi-objective cost function containing multiple amplifier performance indicators based on the overall system analytical mechanical model; The iterative optimization unit 203 is used to iteratively optimize the multi-objective cost function through an optimization algorithm to obtain the target amplifier design parameters; The parameter output unit 204 is used to output the target amplifier design parameters and construct the target low-noise amplifier architecture based on the target amplifier design parameters.
[0067] For specific implementation methods of each of the above units, please refer to the embodiments of the low-noise amplifier architecture design method described above, which will not be repeated here.
[0068] In summary, the low-noise amplifier architecture design apparatus provided in this application can generate a total system analytical mechanical model describing the target low-noise amplifier architecture through the model generation unit 201. This total system analytical mechanical model integrates a first sub-model describing the amplifier's nonlinear dynamics, a second sub-model describing the noise stochastic process, and a third sub-model characterizing circuit stability constraints. Based on the total system analytical mechanical model, the function construction unit 202 constructs a multi-objective cost function containing multiple amplifier performance indicators. The iterative optimization unit 203 iteratively optimizes the multi-objective cost function using an optimization algorithm to obtain the target amplifier design parameters. The parameter output unit 204 outputs the target amplifier design parameters and constructs the target low-noise amplifier architecture based on these parameters. This application embodiment can improve the design efficiency of low-noise amplifier architectures.
[0069] This application also provides an electronic device in which the low-noise amplifier architecture design device of this application can be integrated, such as... Figure 4 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically: The electronic device may include components such as a processor 301 with one or more processing cores and a memory 302 with one or more computer-readable storage media. Those skilled in the art will understand that... Figure 4 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 301 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs stored in the memory 302 and / or this application, and by calling data stored in the memory 302, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operation of the storage medium, user interface, and application programs, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.
[0070] The memory 302 can be used to store software programs and this application. The processor 301 executes various functional applications and data processing by running the software programs and this application stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store applications required for operating the storage medium and at least one function; the data storage area may store data created based on the use of the electronic device. In addition, the memory 302 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.
[0071] Although not shown, the electronic device may also include a display unit, an input unit, and a power supply, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device loads the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 runs the application programs stored in the memory 302 to realize various functions, as follows: Generate a total system analytical mechanical model describing the architecture of the target low-noise amplifier. The total system analytical mechanical model integrates a first sub-model describing the nonlinear dynamics of the amplifier, a second sub-model describing the random noise process, and a third sub-model characterizing the stability constraints of the circuit. Based on the overall system analytical mechanical model, a multi-objective cost function containing multiple amplifier performance indicators is constructed. The multi-objective cost function is iteratively optimized using an optimization algorithm to obtain the design parameters of the target amplifier; Output the target amplifier design parameters and construct the target low-noise amplifier architecture based on the target amplifier design parameters.
[0072] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.
[0073] Therefore, embodiments of this application provide a storage medium storing a plurality of instructions that can be loaded by a processor to execute steps in any of the methods provided in embodiments of this application. For example, the instructions can execute the following steps: Generate a total system analytical mechanical model describing the architecture of the target low-noise amplifier. The total system analytical mechanical model integrates a first sub-model describing the nonlinear dynamics of the amplifier, a second sub-model describing the random noise process, and a third sub-model characterizing the stability constraints of the circuit. Based on the overall system analytical mechanical model, a multi-objective cost function containing multiple amplifier performance indicators is constructed. The multi-objective cost function is iteratively optimized using an optimization algorithm to obtain the design parameters of the target amplifier; Output the target amplifier design parameters and construct the target low-noise amplifier architecture based on the target amplifier design parameters.
[0074] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.
[0075] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.
[0076] Since the instructions stored in the storage medium can execute the steps of any method provided in the embodiments of this application, the beneficial effects that any method provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.
[0077] The above provides a detailed description of the low-noise amplifier architecture design method, apparatus, storage medium, and electronic device provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A low-noise amplifier architecture design method, characterized in that, include: Generate a total system analytical mechanical model describing the architecture of the target low-noise amplifier, wherein the total system analytical mechanical model integrates a first sub-model describing the nonlinear dynamics of the amplifier, a second sub-model describing the random noise process, and a third sub-model characterizing the stability constraints of the circuit; Based on the analytical mechanical model of the overall system, a multi-objective cost function containing multiple amplifier performance indicators is constructed. The multi-objective cost function is iteratively optimized using an optimization algorithm to obtain the target amplifier design parameters; Output the target amplifier design parameters and construct the target low-noise amplifier architecture based on the target amplifier design parameters.
2. The low-noise amplifier architecture design method as described in claim 1, characterized in that, The overall system analytical mechanical model describing the target low-noise amplifier architecture includes: A first sub-model describing the nonlinear dynamics of the amplifier, a second sub-model describing the random noise process, and a third sub-model characterizing the stability constraints of the circuit are generated respectively. The first sub-model, the second sub-model, and the third sub-model are mathematically coupled to form an analytical mechanical model of the overall system.
3. The low-noise amplifier architecture design method as described in claim 2, characterized in that, Generate the first sub-model describing the nonlinear dynamics of the amplifier, including: The nonlinear current-voltage characteristics and nonlinear capacitance characteristics of transistors in amplifier circuits are mapped to nonlinear potential energy functions or nonlinear kinetic energy functions under the analytical mechanical framework. Based on the nonlinear potential energy function or nonlinear kinetic energy function, a nonlinear Hamiltonian or nonlinear Lagrangian describing the nonlinear dynamics of the amplifier is constructed to form the first sub-model.
4. The low-noise amplifier architecture design method as described in claim 2, characterized in that, Generate a second sub-model describing the noisy stochastic process, including: The thermal noise source in the amplifier circuit is modeled as a Gaussian white noise random force coupled to the system's generalized velocity or generalized momentum; The random fluctuation energy corresponding to the random force of Gaussian white noise is introduced into the Hamiltonian or Lagrangian of the analytical mechanics framework to construct the noise Hamiltonian or noise Lagrangian that describes the random process of noise, thus forming the second sub-model.
5. The low-noise amplifier architecture design method as described in claim 2, characterized in that, A third sub-model representing the stability constraints of the circuit is generated, including: Based on the first sub-model and the second sub-model, the system matrix of the overall system analytical mechanical model is derived after linearization near the static operating point; The Nyquist stability criterion or pole location constraint is transformed into a mathematical inequality constraint on the real part of the eigenvalues of the system matrix, thus forming a third sub-model.
6. The low-noise amplifier architecture design method as described in claim 1, characterized in that, The step of iteratively optimizing the multi-objective cost function using an optimization algorithm to obtain the target amplifier design parameters includes: Based on the overall system analytical mechanical model, the gradient information of the multi-objective cost function with respect to the current amplifier design parameters is calculated; Based on the gradient information and the preset step size, the current amplifier design parameters are updated within the feasible region that satisfies the circuit stability constraints. Repeat the gradient calculation and parameter update steps above until the preset convergence condition is met, and output the target amplifier design parameters that meet all performance requirements.
7. The low-noise amplifier architecture design method as described in claim 6, characterized in that, The preset convergence condition includes at least one of the following: The value of the multi-objective cost function changes less than a first threshold in multiple consecutive iterations; The amount of update to the amplifier design parameters in multiple consecutive iterations is less than the second threshold. The number of iterations has reached the preset maximum number of iterations.
8. A low-noise amplifier architecture design device, characterized in that, include: The model generation unit is used to generate a total system analytical mechanical model describing the target low-noise amplifier architecture. The total system analytical mechanical model integrates a first sub-model describing the amplifier's nonlinear dynamics, a second sub-model describing the noise stochastic process, and a third sub-model characterizing the circuit stability constraints. The function construction unit is used to construct a multi-objective cost function containing multiple amplifier performance indicators based on the analytical mechanical model of the overall system. The iterative optimization unit is used to iteratively optimize the multi-objective cost function through an optimization algorithm to obtain the target amplifier design parameters; The parameter output unit is used to output the target amplifier design parameters and construct the target low-noise amplifier architecture based on the target amplifier design parameters.
9. A storage medium, characterized in that, The storage medium stores multiple instructions adapted for loading by a processor to execute the low-noise amplifier architecture design method according to any one of claims 1-7.
10. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the low-noise amplifier architecture design method as described in any one of claims 1-7.