In-pixel integration of signal processing and AI/ML based data filtering for particle tracking detectors

October 8, 2025·
Benjamin parpillon
,
Anthony badea
,
Danush shekar
,
Christian gingu
,
Giuseppe di guglielmo
,
Tom deline
,
Adam quinn
,
Michele ronchi
,
Benjamin weiss
,
Jennet dickinson
,
Jieun yoo
,
Corrinne mills
,
Daniel abadjiev
,
Aidan nicholas
,
Eliza howard
,
Carissa kumar
,
Eric you
,
Mira littmann
,
Karri dipetrillo
,
Arghya ranjan das
,
Mia liu
,
David jiang
,
Mark s. neubauer
,
Morris swartz
,
Petar maksimovic
,
Alice bean
,
Ricardo silvestre
,
Jannicke pearkes
,
Keith ulmer
,
Nick manganelli
,
Chinar syal
,
Doug berry
,
Nhan tran
,
Lindsey gray
,
Farah fahim
· 0 min read
Abstract
We present the first physical realization of in-pixel signal processing with integrated AI-based data filtering for particle tracking detectors. Building on prior work that demonstrated a physics-motivated edge-AI algorithm suitable for ASIC implementation, this work marks a significant milestone toward intelligent silicon trackers. Our prototype readout chip performs real-time data reduction at the sensor level while meeting stringent requirements on power, area, and latency. The chip is taped-out in 28nm TSMC CMOS bulk process, which has been shown to have sufficient radiation hardness for particle experiments. This development represents a key step toward enabling fully on-detector edge AI, with broad implications for data throughput and discovery potential in high-rate, high-radiation environments such as the High-Luminosity LHC.
Type
Publication
arXiv:2510.07485 [physics.ins-det]