Advanced gate-level glitch modeling using ANNs

Multiple Input Switching (MIS) effects commonly induce undesired glitch pulses at the output of CMOS gates, potentially leading to circuit malfunction and significant power consumption. Thus, accurate and efficient glitch modeling is crucial for the design of high-performance, low-power, and reliable ICs. In this work, we present a new gate-level approach for modeling glitch effects under MIS. Unlike previous studies, we leverage efficient Machine Learning (ML) techniques to accurately estimate the glitch shape characteristics, propagation delay, and power consumption. To this end, we evaluate various ML engines and explore different Artificial Neural Network (ANN) architectures. Moreover, we introduce a seamless workflow to integrate our ANNs into existing standard cell libraries, striking an optimal balance between model size and accuracy in gate-level glitch modeling. Experimental evaluation on gates implemented in 7 nm FinFET technology demonstrates that the proposed models achieve an average error of 2.19% against SPICE simulation while maintaining a minimal memory footprint.