A Fast and Efficient Graph-Based Methodology for Cell-Aware Model Generation

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Abstract

As modern Integrated Circuits (ICs) feature ever-smaller transistors, the prevalence of manufacturing defects within standard cells (intra-cell defects) has increased. Detecting and localizing these defects is crucial to guarantee a fast yield ramp-up and to maintain a low-test escape rate. Unfortunately, traditional fault models such as stuck-at and transition fail to adequately represent intra-cell defects. Cell-Aware (CA) was introduced to tackle this problem, but it requires time-consuming analog SPICE simulations for standard cell characterization. To speed-up the CA model generation process, this paper presents a methodology based on graph theory called Transistor Undetectable Defect Eliminator (TrUnDeL). This methodology identifies undetectable defects for each stimulus applied to the inputs of the cell, subsequently excluding them from the analog simulations to perform. TrUnDeL uses rule-based and propagation-based techniques and was trialed on combinational and sequential cells of two 28nm libraries from STMicroelectronics (P28 and C28) to identify the undetectable stimulus/defect pairs, so that analog simulations are performed only on the remaining pairs. As a result, the CA model generation process time was reduced by a factor of 3 compared to a standard SPICE-based generation process.