English(EN) End-to-End Neural Modeling of EM Response and Design Performance for Free-Form RFIC Passives

新神经网络框架加速射频集成电路无源器件设计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了一个新颖的端到端神经网络框架，旨在加速射频集成电路（RFIC）无源匹配网络的设计。该框架可预测不同频率和电路环境下的电磁（EM）响应和下游设计性能，在速度上显著优于传统的电磁仿真方法。该模型在大量无源版图数据集上进行了训练，对大多数目标实现了大于0.98的R^2分数，实现了高精度，并在评估时间上实现了数量级的提升。 AI

影响该神经网络框架为射频集成电路无源器件的设计提供了显著的速度提升，有望加速射频集成电路的开发周期。

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 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yuhao Mao, Chenhao Chu, Martin Vechev, Hua Wang · 2026-10-28 04:00

自由形态射频集成电路无源器件电磁响应与设计性能的端到端神经建模

arXiv:2610.34462v1 Announce Type: cross Abstract: Passive matching networks are critical components in radio-frequency integrated circuits (RFICs), but their design is bottlenecked by expensive finite-difference-based electromagnetic (EM) simulation. We propose an end-to-end neur…

本文如何被排名

报道来源 [1]

自由形态射频集成电路无源器件电磁响应与设计性能的端到端神经建模

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