用物理约束神经网络模拟金刚石探测器电极电阻对快追踪性能的影响
Physics Informed Neural Networks for design optimisation of diamond particle detectors for charged particle fast-tracking at high luminosity hadron colliders
- 构建含电阻效应的三维麦克斯韦方程近似偏微分方程
- 仿真显示电极高阻导致信号延迟，影响纳秒级时间分辨
- 混合专家神经网络实现无网格快速求解，适合辐射强环境设计
未来高亮度强子对撞机需要具有极高辐射耐受性、高空间精度和亚纳秒级时间分辨的跟踪探测器。3D金刚石像素传感器因其抗辐照能力与高载流子迁移率具备上述潜力。但通过飞秒红外激光脉冲制备的导电电极具有高电阻，会延缓信号传播，需扩展经典的Ramo-Shockley加权势理论。本文通过一个三阶、3+1维的偏微分方程（基于麦克斯韦方程的准稳态近似）建模该现象，并在真实3D传感器几何下结合电荷输运仿真进行数值求解。采用基于谱方法数据训练的混合专家物理信息神经网络，实现无网格求解，用于评估电极电阻引起的定时性能退化。
原文摘要 · Abstract (English)
Future high-luminosity hadron colliders demand tracking detectors with extreme radiation tolerance, high spatial precision, and sub-nanosecond timing. 3D diamond pixel sensors offer these capabilities due to diamond's radiation hardness and high carrier mobility. Conductive electrodes, produced via femtosecond IR laser pulses, exhibit high resistivity that delays signal propagation. This effect necessitates extending the classical Ramo-Shockley weighting potential formalism. We model the phenomenon through a 3rd-order, 3+1D PDE derived as a quasi-stationary approximation of Maxwell's equations. The PDE is solved numerically and coupled with charge transport simulations for realistic 3D sensor geometries. A Mixture-of-Experts Physics-Informed Neural Network, trained on Spectral Method data, provides a meshless solver to assess timing degradation from electrode resistance.
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