Real-time self-repairing memory designs that are adaptive and fault-tolerant

Abstract

As modern computing systems grow increasingly complex and operate under harsh conditions, the reliability and resilience of memory components become critical factors affecting overall system performance and longevity. Traditional memory architectures often lack the capability to autonomously detect and correct faults in real time, leading to data corruption, system failures, and reduced operational efficiency. This paper presents a novel design framework for real-time self-repairing memory systems that are inherently adaptive and fault-tolerant, capable of dynamically identifying, isolating, and repairing faults during runtime without interrupting system operation. The proposed architecture integrates built-in self-test (BIST) modules, adaptive error correction codes (ECC), and reconfigurable memory blocks, forming a synergistic system that continuously monitors memory integrity and adjusts repair strategies based on fault patterns and severity. The memory controller leverages machine learning algorithms to predict fault occurrences and proactively reconfigure memory cells, improving fault coverage while minimizing performance overhead. This approach ensures sustained data integrity and system availability even under high fault rates caused by aging, radiation, or manufacturing defects. Comprehensive simulations and hardware-in-the-loop experiments on FPGA prototypes demonstrate that the self-repairing memory design achieves fault detection latencies below 10 microseconds and repair times under 50 microseconds, outperforming conventional ECC-only solutions. The adaptive mechanism reduces uncorrectable error rates by over 60% in high-noise environments, while maintaining throughput degradation below 5%. Furthermore, the system scales effectively with increased memory sizes and diverse fault models, showcasing versatility for applications ranging from embedded systems to large-scale data centers.