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With more advanced semiconductor technologies, identifying process weak points becomes more complex as multiple layers need to be taken into consideration. In recent years, traditional rule based weak point identification has been augmented by pattern matching to pinpoint and fix possible design weak points. Traditional methods of pattern definition are done by profiling the designs for weak points to capture the patterns of interest for applying opportunistic fixes. Patterns are usually handcrafted by taking process information into account, and applying fixes on the design features. Some fail modes have emerged recently that are a result of very complex multi layer interactions. These types of weak points are very difficult to define comprehensively with traditional pattern matching.
Recently, deep learning has undergone a rapid development and tools are now available that can learn based on large amounts of process data. We have harnessed this to address the problem of identifying complex weak points with low escape rates. In this paper, we provide a review on a deep learning based weak point detection flow taking retargeting/opc/orc simulations into account as training data. Using the deep learning approach, the process data is abstracted as an encrypted machine learning model, and released to designers as part of the GLOBALFOUNDRIES (GF) DRC+ tool. This tool is shipped with the PDK, and can be used to fix the design, mitigating process weak points.
This paper begins with a brief introduction to the deep learning TensorFlow model using Convolutional Neural Network (CNN) widely used for image detection. Then we focus on feature density vector (DSV) generation to extract the layout parameters and labels used for training the model. Experimental analysis is also provided to compare recall and precision metrics of POR and ML methods in detecting the weak point on a via layer at process window conditions. Our case study shows that the ML flow improves the pattern capture rate by 34% over standard hotspot detection methods. As a conclusion, we will also brief on our future work leveraging the ML flow for other weak point detections.
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