An elegant hardware-corroborated statistical repair and test methodology for conquering aging effects

We propose a new and efficient statistical-simulation-based test methodology for optimally selecting repair elements at beginning-of-life (BOL) to improve the end-of-life (EOL) functionality of memory designs. This is achieved by identifying the best BOL test/repair corner that maximizes EOL yield, thereby exploiting redundancy to optimize EOL operability with minimal BOL yield loss. The statistical approach makes it possible to identify such corners with tremendous savings in terms of test time and hardware. To estimate yields and search for the best repair corner the approach relies on fast conditional importance sampling statistical simulations. The methodology is versatile and can handle complex aging effects with asymmetrical distributions. Results are demonstrated on state-of-the-art dual-supply memory designs subject to statistical negative bias temperature instability (NBTI) effects, and hardware results are shown to match predicted model trends.