PALTO：面向垂直供电的Tri-Gate FinFET设计优化的物理信息主动学习
PALTO: Physics-Informed Active Learning for Tri-Gate FinFET Design Optimization for Vertical Power Delivery
AI总结：
提出物理信息主动学习框架，高效探索GaN tri-gate FinFET的高维设计空间，优化关键结构参数（如GaN-to-AlGaN厚度比），发现两种优化器件，其中D1在300-fin配置下驱动电流和开关效率优于D2。
摘要：
本文展示了机器学习驱动优化在垂直供电系统中设计特定应用的GaN三栅极FinFET的有效性。传统的基于TCAD的方法计算量大，且不足以导航先进GaN器件的高维非线性设计空间。为此，采用物理信息主动学习框架智能引导仿真，在保持精度的同时加速收敛。这种ML引导的方法通过高效探索关键结构参数——尤其是GaN-to-AlGaN厚度比（器件设计中长期争论的焦点）——来发现最优配置。通过系统探索关键结构参数，确定了两种具有激进缩放的栅漏长度的优化器件。单鳍多通道仿真表明，相对于AlGaN势垒具有更薄GaN沟道的器件D2实现了更高的驱动电流。然而，在300鳍配置中，器件D1以0.49欧姆导通电阻提供3.3A电流，性能约为D2的2倍，尽管寄生参数略高。两种器件均工作在常关模式。基于特定应用品质因数，器件D1达到5 pC·欧姆，开关效率比D2高2倍，而两种设计在不同性能指标上均优于工业基准。
英文摘要：
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 optimal configurations by efficiently exploring key structural parameters -- most notably the GaN-to-AlGaN thickness ratio -- a long-standing focus of debate in device design. By systematically exploring key structural parameters, two optimized devices with aggressively scaled gate-to-drain lengths are identified. Single-fin, multi-channel simulations show that device~D2, with a thinner GaN channel relative to the AlGaN barrier, achieves higher drive current. However, in a 300-fin configuration, device~D1 outperforms device~D2 by delivering 3.3\,A at 0.49~ohm on-resistance -- approximately 2$\times$ better -- despite slightly higher parasitics. Both devices operate in a normally-off mode. Based on an application-specific figure of merit, device~D1 achieves 5\,pC$\cdot$ohm, demonstrating 2$\times$ greater switching efficiency than device~D2, while both designs outperform industrial benchmarks from different performance standpoints.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
