Accelerating Next-Generation Power Technologies with AI-Powered FTCO

Introduction

The power semiconductor industry is at an inflection point. For over three decades, silicon has served as the workhorse material for power electronics, but the relentless push towards higher power densities, faster switching frequencies, and greater efficiency is driving a decisive shift to wide bandgap (WBG) materials. Silicon carbide (SiC) and gallium nitride (GaN) are now firmly established as the technologies of choice for next-generation power applications, spanning electric vehicles (EVs), artificial intelligence (AI) data center infrastructure, and grid-connected renewable energy systems.

The global GaN and SiC power semiconductor market was valued at approximately USD 4.0 billion in 2025 and is projected to reach USD 23.8 billion by 2034, representing a compound annual growth rate (CAGR) of 31.8%. To keep pace with this growth, power device engineers are under increasing pressure to shorten design cycles, reduce physical experimentation, and deliver manufacturable products faster than ever before. This article examines how Silvaco’s AI-powered Fab Technology Co-Optimization (FTCO™) platform, combining simulation at scale, smart Design of Experiments (DOE), and machine learning (ML) surrogate models, is transforming the power device development workflow, with practical demonstrations on pGaN HEMTs, vertical GaN, and SiC DMOS technologies.