Tapas Dutta | Searcharxiv

arXiv2025

A Methodology for Process Design Kit Re-Centering Using TCAD and Experimental Data for Cryogenic Temperatures

In this work, we describe and demonstrate a novel Technology Computer Aided Design (TCAD) driven methodology to re-center room-temperature Process Design Kits (PDKs) for cryogenic operation using a limited set of experimental measurements. Unlike previous approaches that relied on direct fitting of sparse measurements, our technique accounts for process-induced deviations by calibrating TCAD models to both room-temperature and cryogenic data. Compact models for all process corners are extracted from TCAD-generated target characteristics, enabling accurate cryogenic modeling without dedicated foundry support. This scalable, technology-independent method provides a practical path for cryogenic circuit design.

physics.app-ph↗

arXiv2023

Fully Convolutional Generative Machine Learning Method for Accelerating Non-Equilibrium Greens Function Simulations

This work describes a novel simulation approach that combines machine learning and device modelling simulations. The device simulations are based on the quantum mechanical non-equilibrium Greens function (NEGF) approach and the machine learning method is an extension to a convolutional generative network. We have named our new simulation approach ML-NEGF and we have implemented it in our in-house simulator called NESS (nano-electronics simulations software). The reported results demonstrate the improved convergence speed of the ML-NEGF method in comparison to the standard NEGF approach. The trained ML model effectively learns the underlying physics of nano-sheet transistor behaviour, resulting in faster convergence of the coupled Poisson-NEGF simulations. Quantitatively, our ML- NEGF approach achieves an average convergence acceleration of 60%, substantially reducing the computational time while maintaining the same accuracy.

cs.CE↗