Analog Circuit Simulation Error Control Through AI Option Selection
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Solution Overview
Problem
Analog circuit designers face inefficiencies in adjusting error control options during simulation, leading to suboptimal simulation accuracy and speed in circuit design.
Innovation Solution
A method involving determining a simulation measurement object and standard, establishing parameter change rules, performing simulations, and using artificial intelligence to optimize a simulation sampling dataset for error control, enabling automatic selection of optimal simulation options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If designers manually adjust error control options in the simulator, then simulation accuracy can be improved, but the time and effort required increases significantly
Solution Approach 1:
The system performs self-service by automatically selecting optimal error control options through an AI algorithm. The simulator autonomously adjusts simulation parameters based on the measurement object and standard, eliminating the need for manual designer intervention while maintaining high simulation accuracy.
Solution Approach 2:
The invention changes simulation parameters automatically by using an AI algorithm to select optimal error control options. The system dynamically adjusts parameters such as relative error tolerance, absolute error tolerance, and maximum step size based on the specific measurement requirements, rather than manual trial-and-error adjustment.
2Manufacturing precision
If designers repeatedly adjust error control option combinations to achieve satisfactory accuracy, then simulation quality improves, but simulation efficiency deteriorates
Solution Approach 1:
The system implements feedback by using an AI algorithm that learns from simulation results to automatically optimize error control options. The algorithm analyzes the relationship between measurement objects, standards, and optimal parameters, providing feedback-driven parameter selection that improves simulation quality while maintaining efficiency.
Solution Approach 2:
The invention replaces the mechanical manual adjustment process with an intelligent automated system. Instead of designers manually tweaking parameters, an AI algorithm automatically selects optimal error control options based on the measurement requirements, substituting human effort with intelligent automation.
3Measurement precision
If manual adjustment of simulation parameters is performed, then control over simulation accuracy is achieved, but the complexity of the design process increases
Solution Approach 1:
The system performs self-service by automatically selecting optimal error control options through an AI algorithm. The simulator autonomously adjusts simulation parameters based on the measurement object and standard, eliminating the need for manual designer intervention while maintaining high simulation accuracy.
4Productivity
If automatic selection of simulation options is implemented, then productivity improves, but measurement precision may be compromised
Solution Approach 1:
The invention changes simulation parameters automatically by using an AI algorithm to select optimal error control options. The system dynamically adjusts parameters such as relative error tolerance, absolute error tolerance, and maximum step size based on the specific measurement requirements, rather than manual trial-and-error adjustment.
Data Source
AI summary
A method for automatic control of a simulation error in an analog circuit and a use thereof are provided. The method first determines a measurement object and acquires a simulation standard by simulating the measurement object. Then, the method performs simulation based on different parameters of a circuit netlist to acquire an electrical parameter and/or circuit performance evaluation indicator of the measurement object during a simulation process. The method forms a simulation sampling dataset based on a simulation option combination parameter and an error corresponding to the simulation standard. Finally, the method optimizes and analyzes the simulation sampling dataset based on an artificial intelligence algorithm to acquire a simulation option combination that meets a simulation error requirement and takes a least time. The solution achieves automatic selection of the simulation option combination, improving the accuracy of the simulation result.