Machine Learning for CMOS and Beyond CMOS Devices Design | APRIL AI Hub Second Annual Summit скачать в хорошем качестве

Machine Learning for CMOS and Beyond CMOS Devices Design | APRIL AI Hub Second Annual Summit

Twinning Intelligence with Semiconductor Physics This session will begin with the remarks from the Pillar leads explaining what has been achieved during this year and the future course of action. In this session we aim to achieve knowledge dissemination and receive stakeholders’ input. We plan to critically explore into both the CMOS and beyond CMOS devices, which we have been working on and generating useful data sets and generalized models for validation and prediction. This platform is used to showcase the work by our Google-Deepmind research ready interns and our Seed Grant Awardees and collaborators. CMOS device focus- Nanosheet FETs, beyond CMOS device focus- Skyrmions. Recorded and edited by GLO.live Chapters: 0:00 Introduction 0:13:52 CMOS Devices 0:20:03 Skyrmions 0:25:33 Q&A 0:30:29 Integrating Multi-Physics Modelling and Machine Learning in Spintronics 0:34:30 Advancing Beyond CMOS: TCAD Modelling for Circuit Optimisation and System Deployment 0:37:17 Machine Learning for Skyrmion Dynamics: Automating Micromagnetic Simulations 0:39:32 Q&A 0:42:34 Seed Funding Introduction 0:43:26 EPITONIC: Enhancing Performance and Energy Efficiency of Tsetlin Machines through Non-Volatile Computing 0:48:09 Robust Anomaly Detection in Microscopic Images of Integrated Circuits Using Deep Learning Technologies 0:52:11 Q&A 0:55:10 APRIL+ 1:00:34 Google DeepMind Interns Q&A 1:05:11 The Throughline of AI for Electronics 1:09:35 APRIL Community Video 1:12:02 Closing Remarks