Stochastic Testing Simulator for Integrated Circuits and MEMS: Hierarchical and Sparse Techniques

Abstract

Process variations are a major concern in today's chip design since they can significantly degrade chip performance. To predict such degradation, existing circuit and MEMS simulators rely on Monte Carlo algorithms, which are typically too slow. Therefore, novel fast stochastic simulators are highly desired. This paper first reviews our recently developed stochastic testing simulator that can achieve speedup factors of hundreds to thousands over Monte Carlo. Then, we develop a fast hierarchical stochastic spectral simulator to simulate a complex circuit or system consisting of several blocks. We further present a fast simulation approach based on anchored ANOVA (analysis of variance) for some design problems with many process variations. This approach can reduce the simulation cost and can identify which variation sources have strong impacts on the circuit's performance. The simulation results of some circuit and MEMS examples are reported to show the effectiveness of our simulator

Accepted to IEEE Custom Integrated Circuits Conference in June 2014. arXiv admin note: text overlap with arXiv:1407.3023

References in corpus (3)

• Stochastic Testing Method for Transistor-Level Uncertainty Quantification Based on Generalized Polynomial Chaos
• Calculation of Generalized Polynomial-Chaos Basis Functions and Gauss Quadrature Rules in Hierarchical Uncertainty Quantification
• Efficient Uncertainty Quantification for the Periodic Steady State of Forced and Autonomous Circuits

Cited by in corpus (12)

• Enabling High-Dimensional Hierarchical Uncertainty Quantification by ANOVA and Tensor-Train Decomposition
• Enhancing Sparsity of Hermite Polynomial Expansions by Iterative Rotations
• Tensor Computation: A New Framework for High-Dimensional Problems in EDA
• High-Dimensional Uncertainty Quantification of Electronic and Photonic IC with Non-Gaussian Correlated Process Variations
• Stochastic Collocation with Non-Gaussian Correlated Process Variations: Theory, Algorithms and Applications
• Prediction of multi-dimensional spatial variation data via Bayesian tensor completion
• Sliced-Inverse-Regression-Aided Rotated Compressive Sensing Method for Uncertainty Quantification
• A Big-Data Approach to Handle Many Process Variations: Tensor Recovery and Applications
• Uncertainty Quantification of Electronic and Photonic ICs with Non-Gaussian Correlated Process Variations
• Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models
• High-Dimensional Uncertainty Quantification via Tensor Regression with Rank Determination and Adaptive Sampling
• Chance-Constrained and Yield-aware Optimization of Photonic ICs with Non-Gaussian Correlated Process Variations