  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.
