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

Abstract

In this article, a novel machine learning regression-based single-event transient (SET) modeling method is proposed. The proposed method can obtain a reasonable and accurate SET model without introducing complex physical mechanisms, which are not suitable for circuit-level simulation, into the model. To capture the essential physics behind these current transients caused by SET in the circuit-level simulations, we collect plenty of SET current data under different conditions [e.g., different linear energy transfer (LET), different drain bias voltage, different strike position, etc.] to train the SET model. To show the effectiveness of the proposed modeling method, we build a SET pulse current model by learning SET current data of Semiconductor Manufacturing International Corporation (SMIC) 130-nm bulk CMOS obtained by TCAD simulation. A multilayer feedforward neural network is used to build the SET pulse current model and the built SET model takes into account the dependence of time, LETs, drain bias voltages, and strike positions. The results from the model are validated with the simulation from TCAD. The trained SET pulse current model is implemented as a Verilog-A current source in the Cadence Specter 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.