AI/ML Analog Design Synthesis Using Behavioral Models
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Solution Overview
Problem
Traditional analog circuit design synthesis methods are inefficient due to difficulties in reconciling simplified models with industrial-strength simulation environments, lacking adequate coverage of interrelationships among design parameters and sensitivity to process factors, which limits their effectiveness in evolving circuit component values.
Innovation Solution
A processor-implemented method using an AI/ML model to generate behavioral models of analog macros, select suitable components, and synthesize gate-level circuit designs based on a figure of merit (FoM), optimizing the design and performing physical checks to achieve efficient analog circuit design for integrated circuit systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional simulation-based analog design synthesis methods are used, then design validation is achieved, but synthesis efficiency is low due to difficulties in reconciling simplified models with industrial-strength simulation environments
Solution Approach 1:
The system performs preliminary actions by pre-processing simulation data and training machine learning models before actual design synthesis. Behavioral models are generated in advance from circuit schematics and simulation waveforms, creating a library of pre-analyzed design patterns that can be quickly retrieved and applied during synthesis, eliminating the need for repeated full-scale simulations.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between simplified design models and industrial-strength simulation environments. The ML models are trained on simulation data to learn accurate device behavior, then serve as efficient surrogates during synthesis that maintain validation accuracy while dramatically improving synthesis speed.
2Ease of operation
If legacy design methodology is used, then design process is manageable, but coverage of interrelationships among design parameters and sensitivity to process factors is inadequate
Solution Approach 1:
The system automatically explores and optimizes multiple design parameters simultaneously using machine learning algorithms. The ML models evaluate interrelationships among device dimensions, materials, and operating conditions, identifying optimal parameter combinations that satisfy multiple competing requirements while accounting for process variations and sensitivities.
Solution Approach 2:
The system implements feedback loops where simulation results are continuously fed back to refine the machine learning models. Design outcomes and validation data are used to retrain and improve the ML models, enabling them to better capture parameter interrelationships and process sensitivities in subsequent design iterations.
3Adaptability or versatility
If manual design iteration is used, then design flexibility is maintained, but design time increases significantly
Solution Approach 1:
The system enables self-service design synthesis by allowing the machine learning models to automatically generate and evaluate design candidates without requiring manual intervention. The AI-driven synthesis engine independently performs parameter optimization, topology selection, and validation, dramatically reducing design time while maintaining flexibility through configurable design constraints and objectives.
Data Source
AI summary
A system and method for synthesizing analog design in real-time using an artificial intelligence and machine learning (AI/ML) model are provided. The method includes (i) generating a behavioral model of an analog macro using the AI/ML model; (ii) determining one or more operations that is required to implement the behavioral model by scanning the behavioral model; (iii) selecting, using the AI/ML model, the analog macro based on at least one specification that corresponds to the analog macro; (iv) synthesizing, the analog macro that is selected by the AI/ML model 214 and one or more leaf cells for each selected analog macro of the behavioral architectural implementation to obtain a gate-level circuit design based on a figure of merit (FoM) of the analog macro and (v) determining, using the AI/ML model, the analog circuit design for the integrated circuit system based on the gate level circuit design that is synthesized.