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Smart Pixels: Algorithm design and hardware testing for a 28m ROIC for future pixel trackers

ORAL

Abstract

Disentangling the enormous number of particles produced at high energy colliders calls for cutting-edge silicon pixel detectors. These tracking detectors reconstruct the paths of charged particles, an essential experimental task. With billions of readout channels and event rates as high as 40 MHz, these detectors will generate petabytes of data per second. New technologies are needed for ultrafast and power-efficient data extraction. We show here work to design a readout integrated circuit (ROIC) with an on-chip machine learning (ML) algorithm to perform data reduction at the source. This work highlights the algorithm and hardware co-design, giving insight for a future 28nm ROIC implementation.

Publication: Jieun Yoo et. al, Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning, arXiv:2310.02474v1 [physics.ins-det]; Smart Pixels Group, https://fastmachinelearning.org/smart-pixels/

Presenters

  • Carissa N Kumar

    University of Chicago

Authors

  • Carissa N Kumar

    University of Chicago

  • Emily Pan

    UC San Diego

  • Karri F DiPetrillo

    University of Chicago

  • Anthony Badea

    University of Chicago

  • Jennet Dickinson

    Fermilab

  • Jieun Yoo

    University of Illinois Chicago

  • Morris L Swartz

    Johns Hopkins University

  • Giuseppe Di Guglielmo

    Fermilab; Northwestern University

  • Alice L Bean

    University of Kansas

  • Douglas R Berry

    Fermilab

  • Manuel Blanco Valentín

    Northwestern University

  • Farah Fahim

    Fermilab

  • Lindsey A Gray

    Fermilab

  • James F Hirschauer

    Fermilab

  • Shruti Kulkarni

    Oak Ridge National Laboratory

  • Ronald J Lipton

    Fermilab

  • Petar Maksimovic

    Johns Hopkins University

  • Corrinne Mills

    University of Illinois Chicago

  • Benjamin Parpillon

    Fermilab, University of Illinois Chicago

  • Gauri Pradhan

    Fermi National Accelerator Laboratory

  • Nhan V Tran

    Fermilab

  • Aaron Young

    Oak Ridge National Laboratory

  • Chinar Syal

    Fermilab

  • Dahai Wen

    Johns Hopkins University