Learning compact physics‐aware delayed photocurrent models using dynamic mode decomposition

Hanson, Joshua; Bochev, Pavel; Paskaleva, Biliana

doi:10.1002/sam.11485

Learning compact physics‐aware delayed photocurrent models using dynamic mode decomposition

Journal Article · Tue Nov 03 23:00:00 EST 2020 · Statistical Analysis and Data Mining

DOI:https://doi.org/10.1002/sam.11485· OSTI ID:1786596

^[1]; Bochev, Pavel ^[2]; Paskaleva, Biliana ^[3]

Department of Electrical and Computer Engineering University of Illinois at Urbana‐Champaign Urbana Illinois USA
Center for Computing Research Sandia National Laboratories Albuquerque New Mexico USA
Component and System Analysis Sandia National Laboratories Albuquerque New Mexico USA

Abstract

Radiation‐induced photocurrent in semiconductor devices can be simulated using complex physics‐based models, which are accurate, but computationally expensive. This presents a challenge for implementing device characteristics in high‐level circuit simulations where it is computationally infeasible to evaluate detailed models for multiple individual circuit elements. In this work we demonstrate a procedure for learning compact delayed photocurrent models that are efficient enough to implement in large‐scale circuit simulations, but remain faithful to the underlying physics. Our approach utilizes dynamic mode decomposition (DMD), a system identification technique for learning reduced‐order discrete‐time dynamical systems from time series data based on singular value decomposition. To obtain physics‐aware device models, we simulate the excess carrier density induced by radiation pulses by solving numerically the ambipolar diffusion equation, then use the simulated internal state as training data for the DMD algorithm. Our results show that the significantly reduced‐order delayed photocurrent models obtained via this method accurately approximate the dynamics of the internal excess carrier density—which can be used to calculate the induced current at the device boundaries—while remaining compact enough to incorporate into larger circuit simulations.

View Accepted Manuscript (Publisher)

Sponsoring Organization:: USDOE

OSTI ID:: 1786596

Alternate ID(s):: OSTI ID: 1813918

Journal Information:: Statistical Analysis and Data Mining, Journal Name: Statistical Analysis and Data Mining Journal Issue: 6 Vol. 14; ISSN 1932-1864

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: United States

Language:: English

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