Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning

TL;DR

This paper demonstrates a neural network embedded in pixel sensors that filters particle hits in real-time, significantly reducing data volume and enabling advanced high-luminosity collider experiments.

Contribution

It introduces a novel on-sensor deep learning approach for real-time filtering of particle hits, with a custom CMOS circuit design for high-speed, low-power operation.

Findings

Reduces data volume by up to 75%

Neural network embedded in sensor operates at <300 μW

Design demonstrates feasibility for future high-rate detectors

Abstract

Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks. Next-generation detectors require that pixel sizes will be further reduced, leading to unprecedented data rates exceeding those foreseen at the High Luminosity Large Hadron Collider. Signal processing that handles data incoming at a rate of O(40MHz) and intelligently reduces the data within the pixelated region of the detector at rate will enhance physics performance at high luminosity and enable physics analyses that are not currently possible. Using the shape of charge clusters deposited in an array of small pixels, the physical properties of the traversing particle can be extracted with locally customized neural networks. In this first demonstration, we present a neural network that can be embedded into the on-sensor readout and filter out hits from low momentum tracks, reducing the detector's data volume by 54.4-75.4%. The network is designed and simulated as a custom readout integrated circuit with 28 nm CMOS technology and is expected to operate at less than 300 $\mu W$ with an area of less than 0.2 mm$^2$. The temporal development of charge clusters is investigated to demonstrate possible future performance gains, and there is also a discussion of future algorithmic and technological improvements that could enhance efficiency, data reduction, and power per area.