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English(EN) PRISM-UDE: Physics-Regularized Iterative Symbolic Modeling of 3nm FinFETs via Universal Differential Equation

新AI框架为先进芯片创建可解释的晶体管模型

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了PRISM-UDE，一个用于创建准确且物理上可解释的晶体管模型的新框架。该方法将神经网络嵌入到基于物理的模型中，以学习复杂的传输行为，然后将其提炼成封闭形式的符号表达式。当应用于3nm FinFET晶体管时，PRISM-UDE显著降低了预测误差，并在移除神经网络的同时保持了准确性，在电路仿真中展示了稳定的性能。 AI

影响 该框架可能为先进半导体节点的电路仿真带来更稳定和可解释的结果。

排序理由 详细介绍新建模框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新AI框架为先进芯片创建可解释的晶体管模型

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详细介绍新建模框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Pranavanath Balamurali, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar, Rajat Dandekar, Sreedath Panat ·

    PRISM-UDE：基于物理正则化的通用微分方程迭代符号建模3nm FinFETs

    arXiv:2609.13200v1 Announce Type: cross Abstract: Compact transistor models are the mathematical backbone of circuit simulation. However, at advanced nodes such as 3nm, transport physics becomes too complex for traditional hand-derived equations to capture accurately. Purely data…

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