PALTO: Physics-Informed Active Learning for Tri-Gate FinFET Design Optimization for Vertical Power Delivery
arXiv:2606.01265v1 Announce Type: new Abstract: This paper demonstrates the effectiveness of machine learning-driven optimization for designing application-specific GaN tri-gate FinFETs in vertical power delivery systems. Conventional TCAD-based approaches are computationally intensive and insufficient for navigating the high-dimensional, nonlinear design space of advanced GaN devices. To address this, a physics-informed active learning framework is used to intelligently guide simulations, accelerating convergence while preserving accuracy. This ML-guided approach enables the discovery of opti
This publication demonstrates a practical application of AI/ML to optimize semiconductor design, appearing as computational methods become crucial for advancing chip technology amidst increasing complexity.
Advanced FinFET design is critical for next-generation compute, and this AI-accelerated approach promises significant improvements in efficiency and design time for these foundational components of the digital economy.
The conventional, computationally intensive TCAD-based FinFET design process can be significantly accelerated and optimized through physics-informed active learning, leading to faster innovation in advanced semiconductor manufacturing.
- · Semiconductor manufacturers
- · AI/ML in engineering tools
- · GaN device developers
- · High-performance computing sector
- · Traditional TCAD software vendors without AI integration
- · Design teams reliant solely on conventional simulation methods
Machine learning becomes an indispensable tool for optimizing and accelerating the design of complex semiconductor components like FinFETs.
Faster and more efficient FinFET design could lead to more rapid deployment of new chip generations with improved performance and energy efficiency.
The widespread adoption of AI in fundamental engineering design may accelerate the entire compute supply chain, enabling future AI-driven advancements across other sectors.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG