Open AccessRadiation Hardened - AI accelerated Custom IC Design Methodology
Author(s) -
V. Gogolou,
S. Karipidis,
E. Papageorgiou,
A. Michailidis,
T. Noulis
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3574058
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.
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