Enhancing Functional Safety in Automotive AMS Circuits through Unsupervised Machine Learning

no code implementations • 2 Apr 2024 • Ayush Arunachalam, Ian Kintz, Suvadeep Banerjee, Arnab Raha, Xiankun Jin, Fei Su, Viswanathan Pillai Prasanth, Rubin A. Parekhji, Suriyaprakash Natarajan, Kanad Basu

Our approach encompasses a systematic analysis of anomaly abstraction at multiple levels pertaining to the automotive domain, from hardware- to block-level, where anomalies are injected to create diverse fault scenarios.

Anomaly Detection

Machine Learning-enhanced Efficient Spectroscopic Ellipsometry Modeling

no code implementations • 1 Jan 2022 • Ayush Arunachalam, S. Novia Berriel, Parag Banerjee, Kanad Basu

The proposed approach has tremendous implications of faster data acquisition, reduced hardware complexity and easier integration of spectroscopic ellipsometry for in situ monitoring of film thickness deposition.

BIG-bench Machine Learning Drug Discovery