发表机构
Brown University; University of Zurich; Brookhaven National Laboratory; Northwestern University（布朗大学; 苏黎世大学; 布鲁克海文国家实验室; 西北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究基于机器学习的电阻式硅传感器重建与压缩，利用全波形信息规范重建并提取空间信息，引入递归神经网络模型及相关方法，支持任意焊盘，保留位置分辨率，指导传感器设计。
AI 中文摘要
低增益雪崩二极管（LGADs）和交流耦合低增益雪崩二极管（AC-LGADs）是用于精确计时和四维跟踪的有前途的技术。在AC-LGADs中，交流焊盘通过介电层耦合到电阻性n⁺层，而增益层保持不分段。这种结构提供了100%的填充因子，并能在放宽读出间距的情况下实现良好的空间分辨率。使插值成为可能的相同信号共享机制使读出变得复杂：电荷分布在多个焊盘上，有用信息可能接近电子噪声阈值，矩阵求逆方法在计算上可能具有挑战性且对非对角噪声敏感并能在放宽读出间距情况下实现良好的空间分辨率。在这项工作中，我们研究了基于机器学习的电阻式硅传感器重建和压缩。我们使用来自相关焊盘的全波形信息来规范重建，并提取超出二进制读出或幅度减小的摘要所能获得的空间信息。我们首先引入基于LSTM层的递归神经网络模型，该模型为全波形重建提供了概念验证实现，并已使用高层次综合（HLS）进行了FPGA部署测试。我们还研究了通过波形光栅化和窗口选择方法降低带宽的途径，并将该方法扩展到基于拓扑不可知变压器的架构，该架构使用焊盘坐标作为输入的一部分。这些模型旨在支持任意数量和几何形状的焊盘，减轻边缘失真，为500μm×500μm间距的传感器保留约10μm的位置分辨率，并指导未来的电阻式硅传感器设计。
英文摘要
Low-Gain Avalanche Diodes (LGADs) and AC-coupled Low-Gain Avalanche Diodes (AC-LGADs) are promising technologies for precision timing and four-dimensional tracking. In AC-LGADs, the AC pad is coupled to the resistive n$^{+}$ layer through a dielectric layer, while the gain layer remains unsegmented. This structure provides a 100\% fill factor and enables good spatial resolution with a relaxed readout pitch. The same signal-sharing mechanism that makes interpolation possible complicates the readout: charge spreads across multiple pads, the useful information can approach the electronic-noise threshold, and matrix-inversion approaches can become computationally challenging and sensitive to off-diagonal noise. In this work, we study machine-learning-based reconstruction and compression for resistive silicon sensors. We use full-waveform information from correlated pads to regularise the reconstruction and extract spatial information beyond what is available from binary readouts or reduced-amplitude summaries. We first introduce recurrent neural network models based on LSTM layers, which provide a proof-of-concept implementation for full-waveform reconstruction and have been tested for FPGA deployment using \hls. We also study routes towards bandwidth reduction with waveform rasterisation and window-selection methods, and extend the approach beyond the first model to topology-agnostic transformer-based architectures that use pad coordinates as part of the input. These models are designed to support arbitrary pad counts and geometries, mitigate edge distortions, preserve approximately $10~μ\mathrm{m}$ position resolution for $500~μ\mathrm{m}\times500~μ\mathrm{m}$ pitched sensors, and guide future resistive-silicon sensor designs
Journal ref2026 JINST 21 C08015
DOI：10.1088/1748-0221/21/08/C08015
