A Learning-Based Methodology for Accelerating Cell-Aware Model Generation

Cell-aware model generation refers to the process of characterizing cell-internal defects, a key step to ensure high test and diagnosis quality. The main limitation of this process is the generation effort, which is costly in terms of run time, SPICE simulator license usage and flow complexity. in this work, a methodology that does not use any electrical defect simulation is developed to predict the response of a cell-internal defect once it is injected in a standard cell. More widely, the aim is to use existing cell-aware models from various standard cell libraries and technologies to predict cell-aware models for new standard cells independently of the technology. A Random Forest classification algorithm is used for prediction. Experiments on several cell libraries using different technologies demonstrate the accuracy and performance of the method. The paper concludes by the presentation of a new hybrid CA model generation flow.