Large-area Radiation-hard Synchrotron X-ray Detectors
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Perovskite, X-ray Detector,↗
Engineering topics
Deptuch, Grzegorz
Publications and source records attributed to Deptuch, Grzegorz.
Compact, low power, high resolution ADC per pixel for large area pixel detectors
A compact ADC circuit can include one or more comparators, and a serial DAC (Digital-to-Analog) circuit that provides a signal to the comparator (or comparators). In addition, the ADC circuit can include a serial DAC redistribution sequencer that can provide a plurality of signals as input to the serial DAC circuit and is subject to a redistribution cycle and which receives as input a signal from a data multiplexer whose input connects electronically to an output of the comparator. The circuit can further include an ADC code register that provides an ADC output that connects electronically to the output of the comparator and the input to the data multiplexer. Shared logic circuitry for sharing common logic between pixels can be included, wherein the shared logic circuitry connects electronically to the data multiplexer and the ADC code register, wherein the shared logic circuitry promotes area and power savings for the pixel detector circuit.
Fahim, Farah↗
Smart sensors using artificial intelligence for on-detector electronics and ASICs
Cutting edge detectors push sensing technology by further improving spatial and temporal resolution, increasing detector area and volume, and generally reducing backgrounds and noise. This has led to a explosion of more and more data being generated in next-generation experiments. Therefore, the need for near-sensor, at the data source, processing with more powerful algorithms is becoming increasingly important to more efficiently capture the right experimental data, reduce downstream system complexity, and enable faster and lower-power feedback loops. In this paper, we discuss the motivations and potential applications for on-detector AI. Furthermore, the unique requirements of particle physics can uniquely drive the development of novel AI hardware and design tools. We describe existing modern work for particle physics in this area. Finally, we outline a number of areas of opportunity where we can advance machine learning techniques, codesign workflows, and future microelectronics technologies which will accelerate design, performance, and implementations for next generation experiments.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Design and Challenges of Edge Computing ASICs on Front-End Electronics
In situ or hardware-embedded data processing of raw signals, close to their source, in radiation detectors is expected to provide dramatic improvements in data quality and volumes. However, the implementation of artificial neural networks (ANNs) in the front-end electronics, and the design of custom integrated circuits (ASICs), comes with challenges. In addition, detectors have to operate with limited power budget and implement complex functionalities in a very dense space. They often are exposed to extreme conditions as they work in high-radiation environments and/or cryogenic temperatures. This paper presents examples of applications and design methodologies for in-situ ANNs, along with the challenges of retaining the fidelity of the trained networks. For illustration, we use the problem of estimating the energy deposited by the radiation from digitized waveforms. The proposed implementation starts with an ML algorithm trained in Qkeras and eventually leads to an equivalent ASIC implementation. The associated design challenges in realizing energy and area efficient implementations in CMOS processes are reviewed. Novel approaches that employ hybrid technologies (combination of CMOS with memristors), in-memory computing models, and bio-inspired spiking neural networks are also highlighted
47 OTHER INSTRUMENTATION↗