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3 October 2018 Fracture time predictor in mask data preparation using machine learning
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Abstract
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 (R2) over 0.70. The best results were obtained with a 2-layers artificial neural network (ANN) in a standard multi-layer perceptron (MLP) configuration.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Daniel Calderón and Diego Palma "Fracture time predictor in mask data preparation using machine learning", Proc. SPIE 10810, Photomask Technology 2018, 1081005 (3 October 2018); https://doi.org/10.1117/12.2502576
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