# Machine Learning-Based Figure of Merit Model of SIPOS Modulated Drift Region for U-MOSFET

## TL;DR

This paper introduces a machine learning model to optimize the performance of a specific type of MOSFET using SIPOS pillars.

## Contribution

A novel machine learning-based figure of merit model for SJ U-MOSFET with SIPOS pillars is proposed.

## Key findings

- The model optimizes the tradeoff between breakdown voltage and specific ON-resistance.
- Gaussian process regression ensures accurate prediction of the figure of merit.
- The model's validity is confirmed through TCAD simulations.

## Abstract

This paper presents a machine learning-based figure of merit model for superjunction (SJ) U-MOSFET (SSJ-UMOS) with a modulated drift region utilizing semi-insulating poly-crystalline silicon (SIPOS) pillars. This SJ drift region modulation is achieved through SIPOS pillars beneath the trench gate, focusing on optimizing the tradeoff between breakdown voltage (BV) and specific ON-resistance (RON,sp). This analytical model considers the effects of electric field modulation, charge-coupling, and majority carrier accumulation due to additional SIPOS pillars. Gaussian process regression is employed for the figure of merit (FOM = BV2/RON,sp) prediction and hyperparameter optimization, ensuring a reasonable and accurate model. A methodology is devised to determine the optimal BV-RON,sp tradeoff, surpassing the SJ silicon limit. The paper also delves into a discussion of optimal structural parameters for drift region, oxide thickness, and electric field modulation coefficients within the analytical model. The validity of the proposed model is robustly confirmed through comprehensive verification against TCAD simulation results.