[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85342-en":3,"doc-seo-85342-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85342,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Machine Learning-Based Reconstruction for Resistive Silicon Sensors","Machine-learning reconstruction addresses position and timing challenges in resistive silicon sensors, focusing on Low-Gain Avalanche Diodes (LGADs) and AC-coupled LGADs. AC-LGADs exploit 100% fill factor charge sharing by coupling an AC pad to a resistive n+ layer via dielectric, enabling relaxed readout pitch and interpolation. The same sharing complicates readout as charge spreads across pads near noise thresholds. The work uses correlated full-waveform information, LSTM-based recurrent models for FPGA deployment, waveform rasterisation/window selection for bandwidth reduction, and transformer architectures using pad coordinates to support arbitrary geometries while preserving ~10 µm resolution.","arXiv :2607 . 11585v1 [hep-ex] 13 Jul 2026  \nMachine Learning-Based Reconstruction for Resistive Silicon Sensors  \nAlexander Aoki, 1 , Gaetano Barone∗ , 1 , Leena Diehl,2 , Gabriele Giacomini,3 , Vagelis Gkougkousis,2 , Hanshal Goyal, 1 , Rohan Kher, 1 , Daniel Li,4 , Anna Macchiolo,2 , Yevhenii Padnuik,2 , Daria Senina,2 , Samantha Sunnarborg, 1 , Jessica Tang, 1 , Alessandro Tricoli,3 ,  \nLixing Wang 1 , and and Don C. Wong 1  \n1 Brown University, 182 Hope Street, Providence, RI 02912, USA  \n2 University of Zurich  \n3 Brookhaven National Laboratory  \n4 Northwestern University  \nJuly 14, 2026  \nAbstract  \nLow-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 remainsunsegmented. 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 hls4ml. 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 countsand geometries, mitigate edge distortions, preserve approximately 10 µm position resolution for  \n500 µm × 500 µm pitched sensors, and guide future resistive-silicon sensor designs.  \n1 Introduction  \nPrecision timing and spatial resolution are indispensable for next-generation experiments such asthe HL-LHC [1, 2], future e+ e − colliders, and the EIC, where 4D tracking suppresses pile-up, enables time-of-flight PID, and reconstructs dense vertices; similar needs arise in fixed-target [3] and space missions [4, 5], and are highlighted in community roadmaps [6] . Low-Gain Avalanche Diodes (LGADs) and AC-coupled Low-Gain Avalanche Diodes (AC-LGADs) are silicon sensors with internal gain that provide precision timing. LGADs meet timing requirements with O(20– 50) ps performance [7, 8, 9, 10, 11, 12], yet typical ∼ 1 × 1 mm2 pads limit spatial resolution. In the AC-LGAD variant [13, 14], the AC pad is coupled to the resistive n+ layer through a dielectric layer.  \nThe gain layer is not segmented, resulting in a 100% fill factor. This allows the sensor to preserve the timing performance associated with LGAD technology while using charge sharing to obtain spatial resolution with a readout pitch that can be more relaxed than the target spatial precision.  \nWe target high-energy physics applications in low-to-moderate radiation regimes, including the FCC-ee and upgrades to LHC experiments. For the High-Luminosity LHC, possible applications include the LHCb VELO upgrade and a CMS tracker Phase-3 upgrade, where AC-LGADs could  \n∗ Corresponding author.  \nextend timing capabilities in the forward region, increase rapidity coverage, or replace one or two disks with timing-capable instrumentation. For FCC-ee, timing in the outermost silicon layers could enhance particle identification and reduce the systematic uncertainty on beam ene","cbCaiqXWySKFpRnT","https://ap.wps.com/l/cbCaiqXWySKFpRnT","pdf",2765925,1,9,"English","en",105,"# Abstract\n# Introduction\n# Charge sharing in resistive silicon sensors","[{\"question\":\"Why does AC-LGAD design provide both timing benefits and a harder readout problem?\",\"answer\":\"AC-LGADs use an unsegmented gain layer with a 100% fill factor, and charge sharing enables spatial resolution with a more relaxed readout pitch. However, signal sharing spreads charge across multiple pads, making reconstruction a coupled multi-channel problem near the electronic-noise threshold and increasing sensitivity to off-diagonal noise.\"},{\"question\":\"What information do the proposed models use for reconstruction?\",\"answer\":\"They use full-waveform information from correlated pads, which regularizes reconstruction and extracts spatial information beyond what binary readouts or reduced-amplitude summaries provide.\"},{\"question\":\"How do the authors plan to reduce bandwidth while maintaining reconstruction quality?\",\"answer\":\"They study routes such as waveform rasterisation and window-selection methods to reduce the amount of waveform data needed for reconstruction.\"}]",1784202640,23,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"machine-learning-based-reconstruction-for-resistive-silicon-sensors","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-based-reconstruction-for-resistive-silicon-sensors/85342/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why does AC-LGAD design provide both timing benefits and a harder readout problem?","Question",{"text":75,"@type":76},"AC-LGADs use an unsegmented gain layer with a 100% fill factor, and charge sharing enables spatial resolution with a more relaxed readout pitch. However, signal sharing spreads charge across multiple pads, making reconstruction a coupled multi-channel problem near the electronic-noise threshold and increasing sensitivity to off-diagonal noise.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What information do the proposed models use for reconstruction?",{"text":80,"@type":76},"They use full-waveform information from correlated pads, which regularizes reconstruction and extracts spatial information beyond what binary readouts or reduced-amplitude summaries provide.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the authors plan to reduce bandwidth while maintaining reconstruction quality?",{"text":84,"@type":76},"They study routes such as waveform rasterisation and window-selection methods to reduce the amount of waveform data needed for reconstruction.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]