4 papers · 1 filter
Mesh-Native Physics-Informed Graph Surrogates for TCAD-in-the-Loop Design Space Exploration
Leonid Popryho, Ayoub Sadeghi, Inna Partin-Vaisband
High-fidelity TCAD simulation of drift-diffusion transport remains the workhorse of emerging FinFET device design, but it is computationally expensive, especially for 3D structures…
PALTO: Physics-Informed Active Learning for Tri-Gate FinFET Design Optimization for Vertical Power Delivery
Ayoub Sadeghi, Leonid Popryho, Inna Partin-Vaisband
This paper demonstrates the effectiveness of machine learning-driven optimization for designing application-specific GaN tri-gate FinFETs in vertical power delivery systems. Conven…
From Physics to Surrogate Intelligence: A Unified Electro-Thermo-Optimization Framework for TSV Networks
Mohamed Gharib, Leonid Popryho, Inna Partin-Vaisband
High-density through-substrate vias (TSVs) enable 2.5D/3D heterogeneous integration but introduce significant signal-integrity and thermal-reliability challenges due to electrical…
RF-Informed Graph Neural Networks for Accurate and Data-Efficient Circuit Performance Prediction
Anahita Asadi, Leonid Popryho, Inna Partin-Vaisband
Accurately predicting the performance of active radio frequency (RF) circuits is essential for modern wireless systems but remains challenging due to highly nonlinear behavior and…