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DOI | 10.1117/12.2502576 |
Fracture time predictor in mask data preparation using machine learning | |
Calderon, Daniel; Palma, Diego | |
通讯作者 | Calderon, Daniel |
会议名称 | SPIE Photomask Technology Conference |
会议日期 | SEP 17-19, 2018 |
会议地点 | Monterey, CA |
英文摘要 | In Mask Data Preparation (MDP), fracture time can vary from a few seconds to hours or even days. Distributed computing is used to achieve reasonable times for large jobs. To allow more efficient scheduling of the available hardware infrastructure, we need a method to estimate fracture time. Such time estimation is difficult, not only because fracturing in MDP is becoming more complex as technology progresses, but also because fracture time has a direct correlation to the input data, which is a priori unknown. A fracture flow might include data transformations such as scaling, orientation, sizing, and arbitrarily complex Boolean operations among multiple inputs. This complexity provides an opportunity to explore a Machine Learning approach to derive a fracture time prediction model. In this paper we propose a novel machine learning-based method to automatically predict fracture time at the beginning of the process. The approach combines information from the input data and the fracture flow using supervised learning techniques. In particular, to train our machine learning model, we employ a scan of the data, a flow representation and a collection of measured times from real fractures. The work is divided into two parts: a simple fracture of only one input without further processing, and a more general case with several inputs and processes over them. In both cases, our experiments showed that our predictor can achieve low mean squared error estimates and a coefficient of determination (R-2) over 0.70. The best results were obtained with a 2-layers artificial neural network (ANN) in a standard multi-layer perceptron (MLP) configuration. |
英文关键词 | Mask Data Preparation fracturing process machine learning regression Boolean flows supervised learning artificial neural networks MLP multilayer perceptron OASIS |
来源出版物 | PHOTOMASK TECHNOLOGY 2018 |
ISSN | 0277-786X |
EISSN | 1996-756X |
出版年 | 2018 |
卷号 | 10810 |
EISBN | 978-1-5106-2216-6 |
出版者 | SPIE-INT SOC OPTICAL ENGINEERING |
类型 | Proceedings Paper |
语种 | 英语 |
国家 | Chile |
收录类别 | CPCI-S |
WOS记录号 | WOS:000454575200003 |
WOS关键词 | REGRESSION |
WOS类目 | Instruments & Instrumentation ; Optics |
WOS研究方向 | Instruments & Instrumentation ; Optics |
资源类型 | 会议论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/307948 |
作者单位 | Synopsys Chile Ltda, Vitacura 5250, Santiago, Chile |
推荐引用方式 GB/T 7714 | Calderon, Daniel,Palma, Diego. Fracture time predictor in mask data preparation using machine learning[C]:SPIE-INT SOC OPTICAL ENGINEERING,2018. |
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