Machine learning of Physics-InformedGraph Neural Networks from TCAD models
Abstract not provided.
Publications
Machine learning surrogates of high-fidelity electrical models
Abstract not provided.
Extraction of Data-Driven Compact Models from High-Fidelity Semiconductor Simulations with Topological Data Analysis
Abstract not provided.
Physics-informed graph neural network for circuit compact model development
International Conference on Simulation of Semiconductor Processes and Devices, SISPAD
We present a Physics-Informed Graph Neural Network (pigNN) methodology for rapid and automated compact model development. It brings together the inherent strengths of data-driven machine learning, high-fidelity physics in TCAD simulations, and knowledge contained in existing compact models. In this work, we focus on developing a neural network (NN) based compact model for a non-ideal PN diode that represents one nonlinear edge in a pigNN graph. This model accurately captures the smooth transition between the exponential and quasi-linear response regions. By learning voltage dependent non-ideality factor using NN and employing an inverse response function in the NN loss function, the model also accurately captures the voltage dependent recombination effect. This NN compact model serves as basis model for a PN diode that can be a single device or represent an isolated diode in a complex device determined by topological data analysis (TDA) methods. The pigNN methodology is also applicable to derive reduced order models in other engineering areas.
Physics-Informed Graph Neural Network for Circuit Compact Model Development
Abstract not provided.
NextGen Workflow for Electrical Analysis Calibration
Abstract not provided.
6 Results