Soft error rate estimation with TCAD and machine learning

Abstract

We have proposed a neutron-induced soft error rate (SER) estimation method that incorporates machine learning with Monte Carlo radiation transport simulation. Multiple sensitive volumes based machine learning discriminator makes fast SER estimation possible for a unit circuit (e.g. SRAM cell) consisting of several transistors. The discriminator takes charges deposited by a secondary ion to individual volumes of all the transistors as input and outputs the discrimination result, i.e. upset or non-upset. Supervised learning with the training data obtained by TCAD simulations constructs the discriminator. This paper discusses the discriminator construction for 65-nm ultra-thin-box FD-SOI SRAM with TCAD. We experimentally demonstrate the multiple sensitive volumes assignment is useful for building a precise discriminator. We also discuss the critical volumes and transistors for discriminator performance.