Dr. Fadi Salameh Batarseh Profile

Dr. Fadi Salameh Batarseh

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Author

Publications (18)

Proceedings Article | 11 April 2024 Presentation + Paper

In-design DFM pattern optimization with ML models for dynamic fixing guidance selection and implementation

Fadi Batarseh, Piyush Pathak, Jac Condella, Keerthana Bhoopanam, Jeff Nelson, Lynn Wang, Ya-Chieh Lai

Proceedings Volume 12954, 1295406 (2024) https://doi.org/10.1117/12.3023887

KEYWORDS: Design for manufacturing, Design, Machine learning, Mathematical optimization, Manufacturing, Design for manufacturability, Education and training, Process modeling, Histograms, Electronic design automation

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Proceedings Article | 11 April 2024 Presentation + Paper

Pattern-based in-design fixing for improving design for manufacturability (DFM) rule compliance

Lynn T. Wang, Ariel de Jesus Reyes Ruiz, Teodora Nicolae, Ivan Tanev, Klaus-Peter Johnsen, David Villarreal, Fadi Batarseh

Proceedings Volume 12954, 1295410 (2024) https://doi.org/10.1117/12.3010901

KEYWORDS: Design for manufacturing, Metals, Design, Design rules, Manufacturing, Optical proximity correction, Image classification, Automation, Compliance, Design for manufacturability

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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 | 28 April 2023 Presentation + Paper

Design for manufacturability (DFM) in-design fixing for improving manufacturability aware scoring (MAS)

Ariel De Jesus Reyes Ruiz, Lynn T. Wang, Fadi Batarseh, Uwe Paul Schroeder

Proceedings Volume 12495, 124950V (2023) https://doi.org/10.1117/12.2659397

KEYWORDS: Design and modelling, Design for manufacturing, Design rules, Manufacturing, Library classification systems, Image classification, Design for manufacturability, Yield improvement, Target detection, Profiling

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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 | 26 May 2022 Presentation + Paper

Dynamic pattern match searching flow to enable precise hotspot detection

Fadi Batarseh, Uwe Paul Schroeder, Jeff Nelson, Piyush Pathak, Wei-Long Wang, Ya-Chieh Lai

Proceedings Volume 12052, 120520O (2022) https://doi.org/10.1117/12.2617261

KEYWORDS: Data modeling, Machine learning, Silicon, Feature extraction, Optical proximity correction, Metals, Fuzzy logic, Profiling, Databases, Data processing

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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 | 22 February 2021 Presentation + Paper

Fadi Batarseh, Uwe Paul Schroeder, Jeff Nelson, Ya-Chieh Lai, Piyush Pathak, Sriram Madhavan, Philippe Hurat

Proceedings Volume 11614, 1161409 (2021) https://doi.org/10.1117/12.2586112

KEYWORDS: Profiling, Tolerancing, Silicon, Manufacturing, Visualization, Semiconducting wafers, Lithography, Inspection, Design for manufacturability

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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

Dynamic pattern matching flow to enable low escape rate weak point detection

Uwe Paul Schroeder, Janam Bakshi, Ahmed Mounir Elsemary, Fadi Batarseh

Proceedings Volume 11328, 113280D (2020) https://doi.org/10.1117/12.2551739

KEYWORDS: Databases, Image classification, Lithography, Metals, Printing, Digital electronics, Optical proximity correction, Machine learning

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Proceedings Article | 4 April 2018 Presentation + Paper

Litho friendly via insertion with in-design auto-fix flow using machine learning

Ahmed Mounir Elsemary, Moutaz Fakhry, Janam Bakshi, Nishant Shah, Mohamed Ismail, Fadi Batarseh, Uwe Paul Schroeder, Ahmed Mohyeldin, Jason Cain

Proceedings Volume 10588, 105880F (2018) https://doi.org/10.1117/12.2297499

KEYWORDS: Lithography, Metals, Optical lithography, Manufacturing, Machine learning, Image classification, Design for manufacturing, Library classification systems, Pattern recognition

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Proceedings Article | 16 October 2017 Paper

Aerial image ORC checks and their correlation to wafer-edge yield limitation for metals: a study and an OPC resolution

Tamer Desouky, Yixiao Zhang, Mark Terry, Haizhou Yin, Muhammed Pallachali, Nicolai Petrov, Teck Jung Tang, Fadi Batarseh, Ahmed Khalil, Pietro Babighian, Rohan Deshpande, Deborah Ryan, Rao Desineni, Shweta Shokale, Feng Wang, Sang-Kee Eah, Jiechang Hou

Proceedings Volume 10451, 104511L (2017) https://doi.org/10.1117/12.2280568

KEYWORDS: Optical proximity correction, Semiconducting wafers, Silicon, Metals, Design for manufacturing, Scanning electron microscopy, Lithography

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Proceedings Article | 16 March 2016 Paper

Methodology to extract, data mine and score geometric constructs from physical design layouts for analysis and applications in semiconductor manufacturing

Piyush Pathak, Karthik Krishnamoorthy, Wei-Long Wang, Ya-Chieh Lai, Frank Gennari, Shikha Somani, Bob Pack, Uwe Paul Schroeder, Fadi Batarseh, Jaime Bravo, Jason Sweis, Philippe Hurat, Sriram Madhavan

Proceedings Volume 9781, 978109 (2016) https://doi.org/10.1117/12.2220145

KEYWORDS: Design for manufacturing, Mining, Design for manufacturability, Manufacturing, Semiconductor manufacturing, Image classification, Machine learning, Optical proximity correction, Lithography, Semiconductors, Metals, Databases, Charge-coupled devices, Silicon, Data mining, Computer simulations

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Proceedings Article | 18 March 2015 Paper

Hybrid OPC flow with pattern search and replacement

Piyush Verma, Shikha Somani, Yang Ping, Piyush Pathak, Rani Ghaida, Carl Babcock, Fadi Batarseh, Jingyu Wang, Sriram Madhavan, Sarah McGowan

Proceedings Volume 9426, 942611 (2015) https://doi.org/10.1117/12.2087094

KEYWORDS: Optical proximity correction, Photomasks, SRAF, Photovoltaics, Model-based design, Very large scale integration, Optical lithography, Data modeling, Image classification, Library classification systems

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Proceedings Article | 18 March 2015 Paper

VLSI physical design analyzer: A profiling and data mining tool

Shikha Somani, Piyush Verma, Sriram Madhavan, Fadi Batarseh, Robert Pack, Luigi Capodieci

Proceedings Volume 9427, 942712 (2015) https://doi.org/10.1117/12.2087078

KEYWORDS: Statistical analysis, Very large scale integration, Profiling, Data modeling, Databases, Metals, Data mining, Data processing, Data storage, Visualization

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Proceedings Article | 8 October 2014 Paper

Efficient full-chip QA Tool for design to mask (D2M) feature variability verification

Fadi Batarseh, Piyush Verma, Robert Pack, Shikha Somani

Proceedings Volume 9235, 92351Y (2014) https://doi.org/10.1117/12.2069346

KEYWORDS: Profiling, Photomasks, Optical proximity correction, Image classification, Critical dimension metrology, Product engineering, Neodymium, Structural design, Ions, Library classification systems

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Proceedings Article | 8 October 2014 Paper

Model-based virtual VSB mask writer verification for efficient mask error checking and optimization prior to MDP

Robert Pack, Keith Standiford, Todd Lukanc, Guo Xiang Ning, Piyush Verma, Fadi Batarseh, Gek Soon Chua, Akira Fujimura, Linyong Pang

Proceedings Volume 9235, 923509 (2014) https://doi.org/10.1117/12.2069613

KEYWORDS: Photomasks, Optical proximity correction, Lithography, Vestigial sideband modulation, Semiconducting wafers, Data modeling, Model-based design, Error analysis, Manufacturing, Critical dimension metrology

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Proceedings Article | 8 October 2014 Paper

Pattern-based pre-OPC operation to improve model-based OPC runtime

Piyush Verma, Fadi Batarseh, Shikha Somani, Jingyu Wang, Sarah McGowan, Sriram Madhavan

Proceedings Volume 9235, 923506 (2014) https://doi.org/10.1117/12.2068998

KEYWORDS: Optical proximity correction, Photomasks, Very large scale integration, Model-based design, Logic, Lithography, Image classification, Library classification systems, Computer simulations, Optics manufacturing

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Showing 5 of 18 publications

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