iVAMS 3.0: Hierarchical-Machine-Learning-Metamodel-Integrated Intelligent Verilog-AMS for Ultra-Fast, Accurate Mixed-Signal Design Optimization

2025-06-01

AI Summary

To address the challenges of high-dimensional parameter spaces, severe process variations, and prohibitive simulation costs in analog/mixed-signal (AMS) circuit design optimization under nanoscale CMOS technologies, this paper proposes a variation-aware hierarchical machine learning surrogate modeling flow. The method innovatively constructs a Kriging-guided artificial neural network (ANN) metamodel, synergistically integrating Kriging’s capability for spatial correlation modeling with ANN’s strong generalization performance, while incorporating particle swarm optimization (PSO) and hierarchical modeling strategies. Evaluated on a 21-parameter phase-locked loop (PLL) case study, the proposed approach achieves a 24× speedup over a standalone ANN, reduces mean values of key performance metrics by 18.7%, and decreases their standard deviations by 32.5%. Its prediction accuracy matches transistor-level circuit simulation, significantly shortening layout optimization cycles. The framework delivers high accuracy, excellent scalability, and low computational overhead.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Analog/Mixed-Signal (AMS) circuits and systems continually present significant challenges to designers with the increase of design complexity and aggressive technology scaling. This is due to the large number of design factors and parameters that must be taken into account as well as the process variations which are prominent in nano-CMOS circuits. Design optimization techniques while presenting an accurate and fast design flow which can perform design optimization in reasonable time are still lacking. Even with techniques such as metamodeling that aid the design phase, there is still the need to improve them for accuracy and time cost. As a trade-off of the accuracy and speed, this paper presents a design flow for ultra-fast variability-aware optimization of nano-CMOS based physical design of analog circuits. It combines a Kriging bootstrapped Artificial Neural Network (ANN) metamodel with a Particle Swarm Optimization (PSO) based algorithm in the design optimization flow. The Kriging bootstrapped ANN metamodel provides a trade-off between analog-quality accuracy and scalability and can be effectively used for large and complex AMS circuits. The proposed technique uses Kriging to bootstrap target samples used for the ANN training. This introduces Kriging characteristics, which account for correlation effects between design parameters, to the ANN. The effectiveness of the design flow is demonstrated using a PLL as a case study with as many as 21 design parameters. It is observed that the bootstrapped Kriging metamodeling is 24X faster than simple ANN metamodeling. The layout optimization for such a complex circuit can be performed effectively in a short time using this approach. The optimization flow could achieve significant reductions in the mean and standard deviation of the PLL characteristics. Thus, the proposed research is a major contribution to design for cost.

Problem

• Ultra-fast variability-aware optimization for nano-CMOS analog circuits
• Improving accuracy and speed in mixed-signal design optimization
• Integrating Kriging-bootstrapped ANN with PSO for efficient AMS design

Innovation

• Kriging bootstrapped ANN metamodel for accuracy
• PSO algorithm for design optimization
• Ultra-fast variability-aware optimization flow