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Fahim, F.

Publications and source records attributed to Fahim, F..

3D Integrated Sensing Solutions

We will present the proposed research project to develop 3D-integrated sensors using advanced manufacturing capability for novel sensors, heterogeneously integrated with energy efficient readout circuits. The lack of precision timing in particle tracking detectors and the absence of low power, high throughput communications solutions to read them out limits progress in multiple fields of fundamental science. The aim of the project is to develop technology to enable large-scale particle detectors with 3D-integrated ASIC designs to simultaneously achieve 10 μm position resolution and 10 ps precision timing, with low-power consumption and high throughput rates. This research program leverages the unique combination of facilities and cross-disciplinary expertise of scientists and engineers at SLAC, FNAL, and LLNL and industrial partners.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Development of CMOS Sensors for HEP with a US-based foundry

We will present a program to establish the first development and manufacturing of HEP-specific sensors monolithically integrated into a standard CMOS process using a US-based foundry. In collaboration with several US universities the project aims to develop Monolithic Active Pixel Sensors (MAPS) designs implemented in the 90 nm technology node, including simple test structures and multi-pixel arrays, and monolithic CMOS sensors with readout integrated circuits, perform detailed characterization of the detector prototypes and quantify their performance for HEP applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Smart Pixels: towards on-sensor inference of charged particle track parameters and uncertainties

The combinatorics of track seeding has long been a computational bottleneck for triggering and offline computing in High Energy Physics (HEP), and remains so for the HL-LHC. Next-generation pixel sensors will be sufficiently fine-grained to determine angular information of the charged particle passing through from pixel-cluster properties. This detector technology immediately improves the situation for offline tracking, but any major improvements in physics reach are unrealized since they are dominated by lowest-level hardware trigger acceptance. We will demonstrate track angle and hit position prediction, including errors, using a mixture density network within a single layer of silicon as well as the progress towards and status of implementing the neural network in hardware on both FPGAs and ASICs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗