Development, Demonstration, and Validation of Data-driven Compact Diode Models for Circuit Simulation and Analysis
This work addresses the time-consuming and manual effort in compact model development for circuit designers, offering an incremental improvement by applying existing ML methods to a specific domain.
The paper tackled the problem of automating compact semiconductor device model development by exploring three machine learning techniques—table-based interpolation, Generalized Moving Least-Squares, and feed-forward Deep Neural Networks—for a p-n junction diode, resulting in data-driven models validated against laboratory data and circuit simulations.
Compact semiconductor device models are essential for efficiently designing and analyzing large circuits. However, traditional compact model development requires a large amount of manual effort and can span many years. Moreover, inclusion of new physics (eg, radiation effects) into an existing compact model is not trivial and may require redevelopment from scratch. Machine Learning (ML) techniques have the potential to automate and significantly speed up the development of compact models. In addition, ML provides a range of modeling options that can be used to develop hierarchies of compact models tailored to specific circuit design stages. In this paper, we explore three such options: (1) table-based interpolation, (2)Generalized Moving Least-Squares, and (3) feed-forward Deep Neural Networks, to develop compact models for a p-n junction diode. We evaluate the performance of these "data-driven" compact models by (1) comparing their voltage-current characteristics against laboratory data, and (2) building a bridge rectifier circuit using these devices, predicting the circuit's behavior using SPICE-like circuit simulations, and then comparing these predictions against laboratory measurements of the same circuit.