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Radiation-induced photocurrent in semiconductor devices can be simulatedusing complex physics-based models, which are accurate, but computationallyexpensive. This presents a challenge for implementing device characteristics inhigh-level circuit simulations where it is computationally infeasible toevaluate detailed models for multiple individual circuit elements. In this workwe demonstrate a procedure for learning compact delayed photocurrent modelsthat are efficient enough to implement in large-scale circuit simulations, butremain faithful to the underlying physics. Our approach utilizes Dynamic ModeDecomposition (DMD), a system identification technique for learning reducedorder discrete-time dynamical systems from time series data based on singularvalue decomposition. To obtain physics-aware device models, we simulate theexcess carrier density induced by radiation pulses by solving numerically theAmbipolar Diffusion Equation, then use the simulated internal state as trainingdata for the DMD algorithm. Our results show that the significantly reducedorder delayed photocurrent models obtained via this method accuratelyapproximate the dynamics of the internal excess carrier density -- which can beused to calculate the induced current at the device boundaries -- whileremaining compact enough to incorporate into larger circuit simulations.

Learning Compact Physics-Aware Delayed Photocurrent Models Using Dynamic Mode Decomposition