Machine Learning Regression based Single Event Transient Modeling Method for Circuit-Level Simulation

TL;DR

This paper introduces a machine learning regression method using neural networks to model single event transients in circuits, enabling accurate and efficient circuit-level simulations without complex physical modeling.

Contribution

The paper presents a novel neural network-based SET modeling approach trained on TCAD data, simplifying and improving the accuracy of circuit-level transient simulations.

Findings

The neural network model accurately predicts SET pulses from TCAD data.

The model is successfully implemented as a Verilog-A source in circuit simulation.

Simulation results validate the model's effectiveness for circuit-level SEE analysis.

Abstract

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.