Radiation-Hardened—AI-Accelerated Custom IC Design Methodology

In this work, a radiation hardened - AI accelerated custom IC design methodology is proposed. The methodology employs reinforcement learning (RL) to optimize the IC design process, integrating radiation dosage performance degradation assessments and radiation-hardened-by-design (RHBD) MOSFET cell implementations at both schematic and layout levels. This approach streamlines the development of radiation-immune silicon products by embedding artificial intelligence to accelerate the design process and advanced radiation hardening strategies directly into the standard design flow. To validate the proposed framework, a charge-sensitive amplifier (CSA) based on a folded-cascode architecture is designed and assessed in a 180 nm standard CMOS process, demonstrating the efficiency of the methodology in radiation-resilient analog front-end systems.