Arid
DOI10.1117/12.2502576
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
ISSN0277-786X
EISSN1996-756X
出版年2018
卷号10810
EISBN978-1-5106-2216-6
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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