AI-Assisted Self-Healing Electrical Circuit Architectures for Fault Tolerant Smart Electronic Systems

Abstract

Modern solid-state electronic systems operate in increasingly hostile mission critical environments, including aerospace platforms, deep-space probes, autonomous automotive control units, and biomedical implants. These applications require sub-millisecond reliability, zero unscheduled downtime, and long-term operating durability. However, semiconductor degradation mechanisms—such as electromigration, dielectric breakdown, high-energy particle-induced Single Event Upsets (SEUs), thermal cycling stress, and physical micro-fractures—gradually compromise system integrity. Traditional fault-tolerance schemes, primarily based on Static Triple Modular Redundancy (TMR) or passive physical redundancy, introduce massive overhead in silicon area, total mass, power dissipation, and manufacturing complexity without offering physical dynamic recovery. This paper presents a comprehensive review and structural synthesis of AI-Assisted Self-Healing Electrical Circuit Architectures (AI-SHECA) designed for fault-tolerant smart electronic systems. We analyze the convergence of dynamic hardware reconfiguration, soft-computing AI engines, and advanced self-repairing physical materials. By embedding low-latency Machine Learning (ML) inferencing models—such as spiking neural networks (SNNs), lightweight convolutional neural networks (CNNs), and deep reinforcement learning (DRL) algorithms—directly onto field-programmable gate arrays (FPGAs) or embedded neuromorphic co-processors, modern electronic systems can continuously evaluate real-time sensor signatures (current spikes, thermal transients, high-frequency signal jitter, and impedance shifts). The AI engine dynamically predicts catastrophic failures prior to physical breakdown and triggers closed-loop self-healing responses. These responses range from localized micro-fluidic liquid metal micro-channel routing and phase-change material thermal management to autonomous gate-level rerouting and evolutionary algorithm-based hardware topology optimization. Through a comparative review of simulation-based, physical experimental, and hybrid architectural implementations, this paper synthesizes quantitative benchmarks: showing up to a 94.6% restoration of functional circuit performance following catastrophic trace rupture or logic failure, a 68.3% reduction in redundant silicon overhead compared to static TMR, and sub microsecond fault detection-to-repair latencies. The synthesis offers an actionable architectural roadmap for engineering next-generation self sustaining electronic systems capable of surviving harsh operating environments.

KEYWORDS: Self-Healing Circuits, Artificial Intelligence, Fault-Tolerant Systems, Dynamic Hardware Reconfiguration, Neuromorphic Co-processors, Liquid Metal Micro-channels, Spiking Neural Networks.