Search query: Time-series Optimization of Etching Conditions Based on Reinforcement Learning

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abstract: As the structure of semiconductor devices has changed from 2D to miniaturized 3D, the number of process condition parameters has increased for fabrication with nano scale accuracy. For example, in etching process, an etching condition of an etcher, called "recipe" usually has more than 100 parameters because the number of etching steps increases to obtain target etching profiles. Therefore, the process development period increases because the condition with a lot of parameters is difficult to optimize. In order to avoid the rising device cost caused by the prolonged development period, rapid optimization of multi step recipes to obtain target profiles is required. In this study, we propose the method to optimize multi step recipes by utilizing reinforcement learning (RL) which is one of the semi supervised learning methods specialized for time series analysis. To apply RL to etching process, "states" of a control object must be observed during etching. However, our control object, that is, an etching profile cannot be directly observed.
author_names: Hyakka Nakada

Tatehito Usui

Masayoshi Ishikawa

Takeshi Ohmori
title: Data driven Development of Plasma Process Time series Optimization of Etching Conditions Based on Reinforcement Learning
year: 2019

abstract: Today's Web enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package PMTK (probabilistic modeling toolkit) that is freely available online. The book is suitable for upper level undergraduates with an introductory level college math background and beginning graduate students.
author_names: Kevin P Murphy
title: Machine learning a probabilistic perspective
venue: Adaptive computation and machine learning series
year: 2012

abstract: Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received growing attention in the machine learning community over the past decade. The book provides a long needed, systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and classical perspective. Many connections to other well known techniques from machine learning and statistics are discussed, including support vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises. Code and datasets can be obtained on the web. Appendices provide mathematical background and a discussion of Gaussian Markov processes.
author_names: Carl E Rasmussen

Christopher K I Williams
title: Gaussian Processes for Machine Learning
venue: Adaptive computation and machine learning
year: 2009

abstract: Abstract In this paper, three control methods iterative learning control (ILC) repetitive control (RC) and run to run control (R2R) are studied and compared. Some mathematical transformations allow ILC, RC, and R2R to be described in a uniform framework that highlights their similarities. These methods, which play an important role in controlling repetitive processes and run based processes, are collectively referred to as learning type control in this paper. According to the classification adopted in this paper, learning type control has two classes direct form and indirect form The main ideas and designing procedures for these two patterns are introduced, separately. Approximately 400 papers related to learning type control are categorized. Statistical analysis of the resulting data reveals some promising fields for learning type control. Finally, a flowchart based on the unique features of the different methods is presented as a guideline for choosing an appropriate learning type control for different problems.
author_names: Youqing Wang

Furong Gao

Francis J Doyle
title: Survey on iterative learning control, repetitive control, and run to run control
year: 2009

abstract: MISAT is a research and development cluster which will create a small satellite platform based on Micro Systems Technology (MST) aiming at innovative space as well as terrestrial applications. MISAT is part of the Dutch MicroNed program which has established a microsystems infrastructure to fully exploit the MST knowledge chain involving public and industrial partners alike. The cluster covers MST related developments for the spacecraft bus and payload, as well as the satellite architecture. Particular emphasis is given to distributed systems in space to fully exploit the potential of miniaturization for future mission concepts. Examples of current developments are wireless sensor and actuator networks with plug and play characteristics, autonomous digital Sun sensors, re configurable radio front ends with minimum power consumption, or micro machined electrostatic accelerometer and gradiometer system for scientific research in fundamental physics as well as geophysics.
author_names: Eberhard K A Gill

G L E Monna

Jacquelien M A Scherpen

Chris J M Verhoeven
title: MISAT: Designing a Series of Powerful Small Satellites Based upon Micro Systems Technology
year: 2007

abstract: Neural architectures are proposed to model and control plasma etching and deposition processes in semiconductor wafer manufacturing. Static and dynamic neural networks are used to develop plant models and inverse models. A single hidden layer feedforward neural network model learns to identify the system's input output relationship. Another single hidden layer feedforward neural controller learns to model the inverse relationship of the plant. The trained controller, in series with appropriate filters, is then used to control the plasma machine in etching and deposition processes. The paper demonstrates how neural networks can learn both the modeling and control tasks in this nonlinear and complex process.
author_names: Gamze Erten

Ammar B A Gharbi

Fathi M A Salam

Timothy A Grotjohn

Jes Asmussen
title: Using neural networks to control the process of plasma etching and deposition
venue: Proceedings of International Conference on Neural Networks (ICNN'96)
year: 1996

abstract: A model for the topographic influence on shallow landslide initiation is developed by coupling digital terrain data with near surface through flow and slope stability models. The hydrologic model TOPOG (O'Loughlin, 1986) predicts the degree of soil saturation in response to a steady state rainfall for topographic elements defined by the intersection of contours and flow tube boundaries. The slope stability component uses this relative soil saturation to analyze the stability of each topographic element for the case of cohesionless soils of spatially constant thickness and saturated conductivity. The steady state rainfall predicted to cause instability in each topographic element provides a measure of the relative potential for shallow landsliding. The spatial distribution of critical rainfall values is compared with landslide locations mapped from aerial photographs and in the field for three study basins where high resolution digital elevation data are available: Tennessee Valley in Marin County, California; Mettman Ridge in the Oregon Coast Range; and Split Creek on the Olympic Peninsula, Washington. Model predictions in each of these areas are consistent with spatial patterns of observed landslide scars, although hydrologic complexities not accounted for in the model (e.g. spatial variability of soil properties and bedrock flow) control specific sites and timing of debris flow initiation within areas of similar topographic control.
author_names: David R Montgomery

William E Dietrich
title: A physically based model for the topographic control on shallow landsliding
year: 1994

abstract: For many KDD applications, such as detecting criminal activities in E commerce, finding the rare instances or the outliers, can be more interesting than finding the common patterns. Existing work in outlier detection regards being an outlier as a binary property. In this paper, we contend that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier. This degree is called the local outlier factor (LOF) of an object. It is local in that the degree depends on how isolated the object is with respect to the surrounding neighborhood. We give a detailed formal analysis showing that LOF enjoys many desirable properties. Using real world datasets, we demonstrate that LOF can be used to find outliers which appear to be meaningful, but can otherwise not be identified with existing approaches. Finally, a careful performance evaluation of our algorithm confirms we show that our approach of finding local outliers can be practical.
author_names: Markus M Breunig

Hans-Peter Kriegel

Raymond T Ng

Jorg Sander
title: LOF: identifying density based local outliers
venue: SIGMOD '00
year: 2000

abstract: Artistic documents often contain text along curved paths. Commonly used computer algorithms for text extraction, which only deal with text along straight lines, cannot be employed to analyze these artistic documents. In this paper, we solve this problem using the fuzzy curve tracing algorithm. In our method, the character pixels are grouped based on the fuzzy c means algorithm to reduce the amount of data. Then the cluster centers are connected to form the initial curve representing the text path. Finally the character pixels are clustered again under the constraint that the path must be smooth. Results from several experiments are presented to show the effectiveness of our method.
author_names: Hong Yan
title: Detection of curved text path based on the fuzzy curve tracing (FCT) algorithm
venue: Proceedings of Sixth International Conference on Document Analysis and Recognition
year: 2001

abstract: Reinforcement learning algorithms are widely used in many practical artificial intelligence (AI) applications. Herein, we propose a compact hardware based learning system comprising analogue resistive memory devices called "resistive analogue neuromorphic devices (RAND)" We begin by showing that the resistance of a RAND linearly varies with the voltage pulses, meaning that a single RAND is sufficient to solve a reinforcement learning problem (i.e. the "2 armed bandit problem" using "tug of war (TOW) dynamics." Next, we show that 2k armed bandit problems are also solved by hierarchically combining 2k 1 RANDs. Finally, we numerically demonstrate that the proposed methods are promising as compared with conventional methods.
author_names: Song-Ju Kim

Kaori Ohkoda

Masashi Aono

Hisashi Shima

Makoto Takahashi

Yasuhisa Naitoh

Hiroyuki Akinaga
title: Reinforcement Learning System Comprising Resistive Analog Neuromorphic Devices
venue: 2019 IEEE International Reliability Physics Symposium (IRPS)
year: 2019