Patent ID: 12197839

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

In order to more clearly explain the problems solved by the present invention, the technical scheme adopted and the beneficial effects, the detailed description of preferred embodiments of the present invention will be described below with an illustration. The preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention. All modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be within the protection scope of the present invention.

Embodiment 1

As shown inFIG.1, a quick simulation and optimization method for analog circuits comprise a presimulation process and an optimization process.

The presimulation process comprises the steps of constructing a device library, building a circuit, interconnecting an AI network and obtaining a comprehensive parameter;step S1: constructing a device library, wherein the device library comprises a plurality of library units corresponding to different electronic components, and each library unit corresponds to one type of electronic component; a parameterized model and a corresponding AI model are set in each library unit, and the input variable is one or more of a length, a width, a space and a height of different structures in the electronic component, i.e., x1, x2 . . . xn;the parameterized model adopts a designated device described by a computer programming language, and is used for generating a circuit layout containing an actual physical size and a structure of the electronic component by using computer graphics according to an input variable x1, x2 . . . xn; andthe AI model adopts a deep learning network, which is used for predicting a network parameter of the corresponding electronic component according to the same input variable x1, x2 . . . xn as the parameterized model, wherein the network parameter of the electronic component comprises one of a scattering parameter (S parameter), an admittance parameter (Y parameter) and an impedance parameter (Z parameter);step S2: building a circuit;connecting the circuit layout generated by the parameterized model in the plurality of library units with the same type of electronic component or the different types of electronic components according to an actual demand to build a circuit layout of an analog circuit; wherein the type of the input variable of the library unit of the same type of the electronic component is the same or different; and when the type of the input variable of the library unit of the same type of the electronic component is the same, the value of the input variable is the same or different;step S3: interconnecting an AI network;determining interconnection or disconnection of the parameterized model in each corresponding library unit according to an intersection relation of the circuit layout of the parameterized model in each library unit in the circuit layout of the analog circuit, and generating a netlist file describing the interconnection relation of the circuit layout of the plurality of library units; wherein the netlist file describing the interconnection relation of the circuit layout of the plurality of library units in the analog circuit refers to the interconnection of the AI model corresponding to each circuit layout to form the AI network corresponding to the circuit layout of the analog circuit; andthe AI network synchronously makes corresponding modification according to addition, deletion and change of a connection mode of the parameterized model in the circuit layout of the analog circuit without retraining the AI model; according to the needs of circuit design, one or more AI models corresponding to the same parameterized model may be used in the circuit at the same time, and the value of the input variable of each AI model may be the same or different; andstep S4: predicting and generating an S parameter network of each electronic component according to each AI model in the AI network, and finally obtaining a comprehensive network parameter; wherein the specific is as follows:as each AI model in the AI network will generate one S parameter according to the input variable x1, x2 . . . xn, all network parameters in the AI network are calculated by a mathematical method to obtain the comprehensive network parameter, which is output to display by a network parameter curve, or is derived as a data file; andthe optimization process specifically comprises the follow steps of: comparing with a network parameter target of the analog circuit whether the comprehensive network parameter in the simulation process to fall within a threshold range, if falling within the threshold range, outputting the circuit layout of the analog circuit corresponding to the AI network to a three-dimensional full-wave electromagnetic field simulation tool for simulation and verification; and if not falling within the threshold range, using an optimization algorithm to reset a value of the input variable in all or part of the parameterized model in each library unit in the circuit layout of the analog circuit, and carrying out the presimulation process again until the comprehensive network parameter met optimization requirements.

The above-mentioned embodiments are not limitations to the present invention, and the present invention is not limited to the above-mentioned embodiments, so long as the requirements of the present invention are met, the above embodiments shall fall within the protection scope of the present invention.