Sparse Regression Driven Mixture Importance Sampling for Memory Design
Abstract: In this paper, we present a sparse regression (SpaRe) model-based yield analysis methodology and apply it to memory designs with state-of-the-art write-assist circuitry. At the core of its engine is a mixture importance sampling technique which consists of a uniform sampling stage and an importance sampling stage. The proposed methodology allows for fast and accurate statistical analysis of rare fail events. In our approach, a SpaRe model is built using the uniform sampling stage data points obtained via circuit simulation (CktSim). Along with the model, an optimal threshold value is determined for proper pass/fail predict capability. The model and the threshold value are then used to predict the response in the importance sampling stage. This alleviates the need for CktSims in the latter stage and introduces significant speedup compared to fully CktSim-based approaches. The SpaRe model-based yield analysis is tested on a 14-nm FinFET SRAM design, and the results corroborate well with that of full CktSim-based yield analysis. The methodology is used to compare multiple state-of-the-art SRAM designs including selective boost and write-assist designs. The operating Vmin ranges and trends corroborate well with hardware measurements.
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