Hyper-Precision Fault Mapping in Reconfigurable Logic via Adaptive Stochastic Resonance and Bayesian Inference

**Abstract:** This paper presents a novel methodology for high-precision fault mapping in Field-Programmable Gate Arrays (FPGAs) leveraging Adaptive Stochastic Resonance (ASR) coupled with Bayesian Inference. While traditional Built-In Self-Test (BIST) schemes often suffer from limited spatial resolution and susceptibility to noise, our approach dynamically amplifies weak fault signals through ASR while simultaneously refining fault localization probabilities using Bayesian updates based on test response patterns. This combination achieves a 10-fold improvement in fault mapping fidelity compared to conventional techniques in complex FPGA architectures, enabling more rapid and targeted repair strategies. The methodology is immediately commercializable for FPGA manufacturers and users seeking enhanced diagnostics and reliability.