  In this paper, a novel machine learning regression based single event
transient (SET) modeling method is proposed. The proposed method can obtain a
reasonable and accurate model without considering the complex physical
mechanism. We got plenty of SET current data of SMIC 130nm bulk CMOS by TCAD
simulation under different conditions (e.g. different LET and different drain
bias voltage). A multilayer feedfordward neural network is used to build the
SET pulse current model by learning the data from TCAD simulation. The proposed
model is validated with the simulation results from TCAD simulation. The
trained SET pulse current model is implemented as a Verilog-A current source in
the Cadence Spectre circuit simulator and an inverter with five fan-outs is
used to show the practicability and reasonableness of the proposed SET pulse
current model for circuit-level single-event effect (SEE) simulation.
