AI Model for Analog Circuit Simulation Speed
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for simulating and optimizing radio-frequency circuits are limited by the inability to customize circuit models and the slow speed of electromagnetic field simulations, which hinder efficient circuit design and optimization.
Innovation Solution
A quick simulation and optimization method using an AI model, comprising a deep learning network, allows for the creation of customized circuit models with arbitrary structures and rapid prediction of network parameters, improving simulation speed and design efficiency by integrating a parameterized model and AI network for rapid optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electromagnetic field simulation software is used for high accuracy simulation, then simulation accuracy is improved, but simulation time increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-training an AI model using electromagnetic field simulation data before actual circuit design simulations. The AI model is trained offline on a dataset generated from full-wave electromagnetic simulations, storing the mapping between circuit parameters and performance characteristics. During subsequent design iterations, the pre-trained AI model provides rapid predictions without requiring time-consuming electromagnetic simulations, thus resolving the contradiction between accuracy and speed.
Solution Approach 2:
The patent uses copying by creating an AI-based virtual model that replicates the behavior of the physical electromagnetic simulation system. The AI model learns to copy the input-output relationships of electromagnetic field simulations through training on simulation data. Once trained, the AI model serves as a lightweight copy that can predict circuit performance instantaneously, maintaining the accuracy benefits of electromagnetic simulations while eliminating their time consumption.
2Ease of manufacture
If PDK library models are used for circuit simulation, then standardized models are available, but customization of circuit models is limited
Solution Approach 1:
The patent applies dynamics by transitioning from static, fixed PDK library models to a dynamic AI model that can adapt to different circuit configurations and parameters. The AI model is trained on diverse simulation data covering various circuit topologies and parameter combinations, enabling it to dynamically adjust its predictions based on the specific input parameters provided during design, thus achieving both ease of use and high adaptability.
Solution Approach 2:
The patent implements universality by creating a single AI model that can handle multiple types of electronic components and circuit configurations. The model is trained on comprehensive data including various transistor types, capacitor structures, inductor geometries, and different circuit topologies. This universal model replaces the need for multiple specialized PDK models, allowing users to simulate diverse analog circuits with a single tool while maintaining full customization capability.
Data Source
AI summary
Disclosed are a quick simulation and optimization method and system for analog circuits. Aiming at a problem that a customized circuit model is difficultly modeled in a design process of the analog circuit, and the problem of a low circuit design efficiency caused by a slow electromagnetic field simulation speed, the invention proposes to firstly construct a device library, build a circuit and interconnect an AI network to obtain a comprehensive network parameter of the AI network; the comprehensive network parameter in a simulation process is compared with a network parameter target of an analog circuit, and an circuit layout of the analog circuit corresponding to the AI network is output to a three-dimensional full-wave electromagnetic field simulation tool for simulation and verification when requirements are met.