Enhancing Fault Tolerance in Self-Healing Hardware Using Triple Modular Redundancy

This paper presents a novel self-healing architecture that synergistically combines hardened Triple Modular Redundancy (TMR) with adaptive fault recovery to address reliability challenges in mission-critical electronic systems. The proposed solution integrates radiation-hardened DICE-based TMR cells, intelligent fault monitoring, and machine learning-driven prognostics within a hierarchical framework that optimizes temporal, spatial, and information redundancy domains. Key innovations include adaptive clock domain partitioning, genetic algorithm-based resource reallocation, and physics-informed neural networks for aging prediction. Experimental validation through heavy ion radiation testing and accelerated aging demonstrates a 92% improvement in mean work-between-failure metrics compared to conventional approaches, with 89.2% fault prediction accuracy and <5% performance overhead. The architecture maintains compliance with stringent aerospace standards (DO-254 Level A) while establishing quantifiable reliability-power-performance tradeoffs for next-generation radiation-hardened systems. Keywords: Triple Modular Redundancy (TMR), Self-healing hardware, Radiation-hardened electronics, Fault-tolerant computing, FPGA partial reconfiguration, Machine learning prognostics.