A Systematic Taxonomy and Comparative Analysis of Mixed-Signal Simulation Methods: From Classical SPICE to AI-Enhanced Approaches
Year: 2026
Type: article
Abstract: Mixed-signal simulation is indispensable for verifying modern integrated circuits that tightly couple analog and digital subsystems, yet the field lacks a unified framework for systematically comparing its diverse methodologies. This paper addresses that gap by proposing a novel three-axis taxonomy that classifies simulation methods along abstraction level, solver methodology, and analysis type, together with a comparative evaluation framework based on five quantitative metrics: accuracy, throughput, capacity, convergence reliability, and scalability. Applying this framework, we systematically compare thirteen classical method categories—spanning SPICE, FastSPICE, RF/periodic steady-state, behavioral modeling, co-simulation, and model order reduction—and eight AI/ML approaches including Gaussian process surrogates, graph neural networks, physics-informed neural networks, Bayesian optimization, and reinforcement learning. Our analysis reveals a clear maturity stratification: classical methods remain the only signoff-accurate approaches, Bayesian optimization represents the most industrially validated AI contribution with integration across all three major EDA platforms, while Neural ODE solvers and LLM-based design tools remain at the research stage. We identify a persistent academic-to-industry gap driven by foundry model complexity, limited benchmark diversity, and topology-specific overfitting. The proposed taxonomy and comparative framework provide practitioners with structured guidance for simulation method selection and highlight specific research directions needed to bridge the gap between AI promise and industrial deployment.
Source: Electronics
Authors: Jian Yu, Hairui Zhu, Jiawen Yuan, Lei Jiang
Institution: Nanjing Institute of Technology
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7154622613