Simulation verification of adaptive dead time control for radiation detectors based on reinforcement learning and FPGA

Wen Li; Xing Huang; Yu Min Liu; Yu Sun; Xiang Ting Meng

doi:10.1515/kern-2026-0024

August 14, 2026KerntechnikOpen Access

Simulation verification of adaptive dead time control for radiation detectors based on reinforcement learning and FPGA

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Key Points

To resolve severe data loss caused by system dead time in dynamic, high-flux radiation monitoring systems using an adaptive circuit-algorithm-hardware co-design.
Simulated signal conditioning circuits in LTspice with noise, temperature, and parasitic parameters, extracting dead time constraints using dual-pulse tests and Monte Carlo simulations.
Trained a reinforcement learning model within a Markov Decision Process framework and compressed the policy into a hardware-friendly finite state machine lookup table.
Verified the compressed adaptive controller architecture within an FPGA development environment.
Achieved timing convergence with minimal resource utilization when mapped onto FPGA hardware.
Improved total radiation detection efficiency by approximately 21.07% at 1.5 MHz compared to a traditional fixed dead-time strategy.

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Implication

Simulation study demonstrates a 21.07% efficiency gain in radiation detectors, indicating improved counting accuracy in dynamic high-flux environments.

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Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/6a8179abf2fb91fc834acf25 https://doi.org/10.1515/kern-2026-0024

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Simulation verification of adaptive dead time control for radiation detectors based on reinforcement learning and FPGA

Key Points

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Cite This Study

Simulation verification of adaptive dead time control for radiation detectors based on reinforcement learning and FPGA

Key Points

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Implication

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Cite This Study

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