Intelligent Pixel Detectors: Towards a Radiation Hard ASIC with On-Chip Machine Learning in 28 nm CMOS
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arXiv
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| Format: | Preprint |
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2024
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| author | Badea, Anthony Bean, Alice Berry, Doug Dickinson, Jennet DiPetrillo, Karri Fahim, Farah Gray, Lindsey Di Guglielmo, Giuseppe Jiang, David Kovach-Fuentes, Rachel Maksimovic, Petar Mills, Corrinne Neubauer, Mark S. Parpillon, Benjamin Shekar, Danush Swartz, Morris Syal, Chinar Tran, Nhan Yoo, Jieun |
| author_facet | Badea, Anthony Bean, Alice Berry, Doug Dickinson, Jennet DiPetrillo, Karri Fahim, Farah Gray, Lindsey Di Guglielmo, Giuseppe Jiang, David Kovach-Fuentes, Rachel Maksimovic, Petar Mills, Corrinne Neubauer, Mark S. Parpillon, Benjamin Shekar, Danush Swartz, Morris Syal, Chinar Tran, Nhan Yoo, Jieun |
| contents | Detectors at future high energy colliders will face enormous technical challenges. Disentangling the unprecedented numbers of particles expected in each event will require highly granular silicon pixel detectors with billions of readout channels. With event rates as high as 40 MHz, these detectors will generate petabytes of data per second. To enable discovery within strict bandwidth and latency constraints, future trackers must be capable of fast, power efficient, and radiation hard data-reduction at the source. We are developing a radiation hard readout integrated circuit (ROIC) in 28nm CMOS with on-chip machine learning (ML) for future intelligent pixel detectors. We will show track parameter predictions using a neural network within a single layer of silicon and hardware tests on the first tape-outs produced with TSMC. Preliminary results indicate that reading out featurized clusters from particles above a modest momentum threshold could enable using pixel information at 40 MHz. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02945 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Intelligent Pixel Detectors: Towards a Radiation Hard ASIC with On-Chip Machine Learning in 28 nm CMOS Badea, Anthony Bean, Alice Berry, Doug Dickinson, Jennet DiPetrillo, Karri Fahim, Farah Gray, Lindsey Di Guglielmo, Giuseppe Jiang, David Kovach-Fuentes, Rachel Maksimovic, Petar Mills, Corrinne Neubauer, Mark S. Parpillon, Benjamin Shekar, Danush Swartz, Morris Syal, Chinar Tran, Nhan Yoo, Jieun Instrumentation and Detectors High Energy Physics - Experiment Detectors at future high energy colliders will face enormous technical challenges. Disentangling the unprecedented numbers of particles expected in each event will require highly granular silicon pixel detectors with billions of readout channels. With event rates as high as 40 MHz, these detectors will generate petabytes of data per second. To enable discovery within strict bandwidth and latency constraints, future trackers must be capable of fast, power efficient, and radiation hard data-reduction at the source. We are developing a radiation hard readout integrated circuit (ROIC) in 28nm CMOS with on-chip machine learning (ML) for future intelligent pixel detectors. We will show track parameter predictions using a neural network within a single layer of silicon and hardware tests on the first tape-outs produced with TSMC. Preliminary results indicate that reading out featurized clusters from particles above a modest momentum threshold could enable using pixel information at 40 MHz. |
| title | Intelligent Pixel Detectors: Towards a Radiation Hard ASIC with On-Chip Machine Learning in 28 nm CMOS |
| topic | Instrumentation and Detectors High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2410.02945 |