Accelerating Semiconductor Innovation through Machine Learning-Driven Modeling
白皮书
The semiconductor industry is entering an era of unprecedented complexity, driven by advanced architectures such as Gate-All-Around (GAA) transistors, wide-bandgap materials like GaN and SiC, and heterogeneous integration strategies. Traditional physics-based modeling approaches are increasingly challenged by nonlinear effects, electro-thermal interactions, and variability across device geometries and conditions. Manual parameter extraction workflows, often requiring hundreds of steps, slow development and limit scalability.
This white paper introduces AI and Machine Learning (ML) as transformative solutions for semiconductor device modeling and parameter extraction. We explore three key approaches: neural network-based modeling, which learns device behavior directly from data; hybrid neural network architectures, which combine physical models with data-driven adaptability; and ML-driven extraction workflows, which reduce hundreds of manual steps to fewer than ten. These methods deliver faster, more accurate, and scalable modeling across DC, RF, and large-signal domains.
Benchmarks demonstrate significant gains: neural and hybrid models achieve excellent agreement with measured data for DC characteristics, broadband S-parameters, and large-signal performance metrics such as gain compression and Power-Added Efficiency (PAE). These improvements accelerate Process Design Kit (PDK) delivery from weeks to days, reduce reliance on expert intervention, and enable reusable flows across technology nodes.
In summary, AI and ML are not just enhancing semiconductor modeling—they are redefining it. By adopting intelligent modeling frameworks today, organizations can achieve faster, more accurate, and future-ready design processes.