Verification of directed self-assembly (DSA) guide patterns through machine learning
| Autor: | Byung-Il Choi, Jae-Won Yang, Youngsoo Shin, Seongbo Shim, Sibo Cai, Seung-Hune Yang |
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| Rok vydání: | 2015 |
| Předmět: |
Series (mathematics)
business.industry Computer science Process (computing) Sample (statistics) Function (mathematics) Construct (python library) Type (model theory) Space (commercial competition) Machine learning computer.software_genre Global Positioning System Artificial intelligence business computer |
| Zdroj: | Alternative Lithographic Technologies VII. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.2085644 |
| Popis: | Verification of full-chip DSA guide patterns (GPs) through simulations is not practical due to long runtime. We develop a decision function (or functions), which receives n geometry parameters of a GP as inputs and predicts whether the GP faithfully produces desired contacts (good) or not (bad). We take a few sample GPs to construct the function; DSA simulations are performed for each GP to decide whether it is good or bad, and the decision is marked in n -dimensional space. The hyper-plane that separates good marks and bad marks in that space is determined through machine learning process, and corresponds to our decision function. We try a single global function that can be applied to any GP types, and a series of functions in which each function is customized for different GP type; they are then compared and assessed in 10nm technology. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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