Ms. Sarah Rizk Profile

Sarah Rizk

at Siemens EDA

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Publications (4)

Proceedings Article | 28 April 2023 Poster + Paper

Design rule manual and DRC code qualification flows empowered by high coverage synthetic layouts generation

Aliaa Kabeel, Sutae Kim, Young Gook Park, Donggyun Kim, Joe Kwan, Sarah Rizk, Kareem Madkour, Marwa Shafee, Jinhee Kim

Proceedings Volume 12495, 124951T (2023) https://doi.org/10.1117/12.2658350

KEYWORDS: Design rules, Design and modelling, Error analysis, Metals, Computer aided design, Solids, Electronic design automation, Computer programming, Statistical analysis, Standards development

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The Design Rule Manual (DRM) is a critical component in the introduction and release of new technology nodes. It is the reference manual of definitive requirements, documented in detail, on all information regarding design rules and technology node design requirements. The DRM is a contract between the foundry and the designer. Designs must meet all documented requirements to be accepted for manufacture. The DRM’s critical role in process design enablement obligates it to a very high quality standard. The DRM must be accurate, reliable, and clear of ambiguity. Qualification of the DRM is crucial as design rules become extremely complex with advancing technology. DRM teams must ensure all descriptions and figures are correct and clear versus target requirements from the beginning of the technology development stage. The qualification process should cover all typical cases as well as corner and unexpected cases. Traditional methods of targeted pattern creation leave gaps in ensuring a high quality DRM. Those methods often miss complex scenarios leading to incomplete DRM documentation or descriptions with vague ambiguity. Ambiguity in the DRM leads to improper DRC rule coding, resulting in erroneous DRC checking. This paper presents a synthetic pattern/layout generation approach to high quality and high coverage DRM and DRC qualification. The generated patterns flow into a post-generation-analysis-fix step that helps discover and analyze issues while initial design rules and DRC code is being developed. Guided random generation of legal layout patterns produces simple and complex pattern configurations to challenge the accuracy and consistency between the original intention of the complex design rules and DRC rule deck. The post-generation-analysis-fix step helps identify locations of potential discrepancy. Flushing out these discrepancies and ambiguities drives enhancements to converge on robust DRM documentation and consistency between design rule intent and DRC run set implementation from early development throughout the life cycle of process node deployment.

Proceedings Article | 26 May 2022 Paper

Robust qualification flows for DRC decks using high coverage synthetic layout generation

Ramy Abdelmeged, Srinivas Balagowni, Ahmed Abdelaleem, Joe Kwan, Aliaa Kabeel, Sarah Rizk, Ahmed Abouzeid

Proceedings Volume 12052, 120520L (2022) https://doi.org/10.1117/12.2614085

KEYWORDS: Legal, Manufacturing, New and emerging technologies, Monte Carlo methods, Metals, Error analysis, Design for manufacturability, Design for manufacturing

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Proceedings Article | 23 March 2020 Presentation + Paper

Machine Learning using retarget data to improve accuracy of fast lithographic hotspot detection

Aliaa Kabeel, Wael ElManhawy, Joe Kwan, Asmaa Rabie, Mohamed Ismail, Ahmed Khater, Sarah Rizk

Proceedings Volume 11328, 1132803 (2020) https://doi.org/10.1117/12.2551827

KEYWORDS: Data modeling, Machine learning, Lithography, Manufacturing, Metals, Critical dimension metrology, Silicon, Photomasks, Etching, SRAF

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Proceedings Article | 23 March 2020 Paper

Approaches for full coverage physical design space exploration and analysis by synthetic layout generation

Marwah Shafee, Joe Kwan, Wael El-Manhawy, Aliaa Kabeel, Sarah Risk, Jongha Park, SeungJo Lee, Jin Hee Kim, Mostafa Alaa, Mohamed Bahnasawi, Abdelrahman Abdelrazek, Sherif Hammouda, Kareem Madkour, Nabil Sabry

Proceedings Volume 11328, 1132808 (2020) https://doi.org/10.1117/12.2551916

KEYWORDS: Metals, Product engineering, Monte Carlo methods, Image classification, Library classification systems, Classification systems, Process engineering, Manufacturing

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