Mr. Pouya Rezaeifakhr Profile

Pouya Rezaeifakhr

at GlobalFoundries

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Publications (6)

Proceedings Article | 28 April 2023 Presentation + Paper

Machine learning architecture evaluation for fast and accurate weak point detection

Yi Lee, Uwe Paul Schroeder, Pouya Rezaeifakhr, Lynn Wang, David Villarreal

Proceedings Volume 12495, 1249519 (2023) https://doi.org/10.1117/12.2657156

KEYWORDS: Design and modelling, Design for manufacturing, Design rules, Machine learning, Data modeling, Statistical modeling, Statistical analysis, Chip manufacturing

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Proceedings Article | 28 April 2023 Presentation + Paper

Design for manufacturability (DFM) physical verification using machine learning

Lynn T. Wang, Klaus-Peter Johnsen, Ivan Tanev, Fadi Batarseh, Chang Su, Pouya Rezaeifakhr, Uwe Paul Schroeder

Proceedings Volume 12495, 124951G (2023) https://doi.org/10.1117/12.2658646

KEYWORDS: Data modeling, Machine learning, Neural networks, Design and modelling, Design for manufacturing, Principal component analysis, Correlation coefficients, Singular value decomposition, Mathematical optimization, Lithography

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Proceedings Article | 26 May 2022 Presentation + Paper

Retargeting-aware design for manufacturability (DFM) checks using machine learning

Lynn T. Wang, Uwe Paul Schroeder, Punitha Selvam, Fadi Batarseh, Pouya Rezaeifakhr, Ariel de Jesus Reyes, Teodora Nicolae, Ivan Tanev, Sriram Madhavan

Proceedings Volume 12052, 1205211 (2022) https://doi.org/10.1117/12.2614460

KEYWORDS: Design for manufacturing, Machine learning, Feature extraction, Metals, Computer aided design, Data modeling, Manufacturing, Neural networks, Design for manufacturability, Data processing

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Proceedings Article | 26 May 2022 Presentation + Paper

Machine learning architecture evaluation for fast and accurate weak point detection

Suraag Sunil Tellakula, Uwe Paul Schroeder, Janam Bakshi, Punitha Selvam, Fadi Batarseh, Pouya Rezaeifakhr, Sriram Madhavan

Proceedings Volume 12052, 120520K (2022) https://doi.org/10.1117/12.2613828

KEYWORDS: Computational lithography, Immersion lithography, Data modeling, Machine learning, Neurons, Design for manufacturing, Neural networks

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Proceedings Article | 22 February 2021 Presentation + Paper

Deep learning-based hotspot prediction of via printability in process window corners

Punitha Selvam, Pouya Rezaeifakhr, Uwe Paul Schroeder, Janam Bakshi, Omnia Mohamed, Fadi Batarseh, Sriram Madhavan

Proceedings Volume 11614, 116140X (2021) https://doi.org/10.1117/12.2583662

KEYWORDS: Data processing, Data modeling, Multilayers, Dynamic signature verification, Semiconductors, Profiling, Machine learning, Design for manufacturing, Convolutional neural networks

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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.

Proceedings Article | 23 March 2020 Presentation + Paper

Foundry approach for layout risk assessment through comprehensive pattern harvesting and large-scale data analysis

Monisa Ramesh Babu, Shenghua Song, Qian Xie, Pouya Rezaeifakhr, Eric Chiu, Joo Hyun Park, Deborah Ryan, Kiruthika Murali, Praneetha Poluju, Shobhit Malik, Haizhou Yin, Sriram Madhavan, Panneerselvam Venkatachalam

Proceedings Volume 11328, 113280H (2020) https://doi.org/10.1117/12.2551939

KEYWORDS: Inspection, Silicon, Machine learning, Databases, Scanning electron microscopy, Data modeling, Manufacturing, Data analysis, Analytics, Semiconducting wafers

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