Machine Learning Based Modelling In Atomic Layer Deposition Processes
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Machine Learning Based Modelling In Atomic Layer Deposition Processes
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Author : Oluwatobi Adeleke
language : en
Publisher: CRC Press
Release Date : 2023-12-15
Machine Learning Based Modelling In Atomic Layer Deposition Processes written by Oluwatobi Adeleke and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-15 with Technology & Engineering categories.
While thin film technology has benefited greatly from artificial intelligence (AI) and machine learning (ML) techniques, there is still much to be learned from a full-scale exploration of these technologies in atomic layer deposition (ALD). This book provides in-depth information regarding the application of ML-based modeling techniques in thin film technology as a standalone approach and integrated with the classical simulation and modeling methods. It is the first of its kind to present detailed information regarding approaches in ML-based modeling, optimization, and prediction of the behaviors and characteristics of ALD for improved process quality control and discovery of new materials. As such, this book fills significant knowledge gaps in the existing resources as it provides extensive information on ML and its applications in film thin technology. Offers an in-depth overview of the fundamentals of thin film technology, state-of-the-art computational simulation approaches in ALD, ML techniques, algorithms, applications, and challenges. Establishes the need for and significance of ML applications in ALD while introducing integration approaches for ML techniques with computation simulation approaches. Explores the application of key techniques in ML, such as predictive analysis, classification techniques, feature engineering, image processing capability, and microstructural analysis of deep learning algorithms and generative model benefits in ALD. Helps readers gain a holistic understanding of the exciting applications of ML-based solutions to ALD problems and apply them to real-world issues. Aimed at materials scientists and engineers, this book fills significant knowledge gaps in existing resources as it provides extensive information on ML and its applications in film thin technology. It also opens space for future intensive research and intriguing opportunities for ML-enhanced ALD processes, which scale from academic to industrial applications.
Handbook Of Manufacturing Engineering And Technology
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Author : Andrew Y. C. Nee
language : en
Publisher: Springer
Release Date : 2014-10-31
Handbook Of Manufacturing Engineering And Technology written by Andrew Y. C. Nee and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-31 with Technology & Engineering categories.
The Springer Reference Work Handbook of Manufacturing Engineering and Technology provides overviews and in-depth and authoritative analyses on the basic and cutting-edge manufacturing technologies and sciences across a broad spectrum of areas. These topics are commonly encountered in industries as well as in academia. Manufacturing engineering curricula across universities are now essential topics covered in major universities worldwide.
Microscopic Modeling Machine Learning Based Modeling And Optimal Operation Of Thermal And Plasma Atomic Layer Deposition
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Author : Yangyao Ding
language : en
Publisher:
Release Date : 2021
Microscopic Modeling Machine Learning Based Modeling And Optimal Operation Of Thermal And Plasma Atomic Layer Deposition written by Yangyao Ding and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Atomic layer deposition (ALD) and plasma enhanced atomic layer deposition (PEALD) are the most widely utilized deposition techniques in the semiconductor industry due to their superior ability to produce highly conformal films and to deposit materials into high aspect-ratio geometric structures. Additionally, plasma enhanced ALD is able to further speed up the deposition process and to reduce the temperature requirement through the utilization of high energy particles. However, ALD and PEALD experiments remain expensive and time-consuming, and the existing first-principles based models have not yet been able to provide solutions to key process outputs that are computationally efficient, which is necessary for on-line optimization and real-time control. Motivated by the above considerations, this dissertation focuses on addressing these issues for both ALD and PEALD. First, for ALD, the development of key components of a comprehensive simulation framework is presented. The simulation framework integrates first-principles-based microscopic modeling, input/output modeling and optimal operation of thermal atomic layer deposition (ALD) of SiO2 thin-films using bis(tertiary-butylamino)silane (BTBAS) and ozone as precursors. Specifically, we initially utilize Density Functional Theory (DFT)-based calculations for the computation of the key thermodynamic and kinetic parameters, which are then used in the microscopic modeling of the ALD process. Subsequently, a detailed microscopic model is constructed, accounting for the microscopic lattice structure and atomic interactions, as well as multiple microscopic film growth processes including physisorption, abstraction and competing chemical reaction pathways. Kinetic Monte-Carlo (kMC) algorithms are utilized to obtain computationally efficient microscopic model solutions while preserving model fidelity. The obtained kMC simulation results are used to train Artificial Neural Network (ANN)-based data-driven models that capture the relationship between operating process parameters and time to ALD cycle completion. Specifically, a two-hidden-layer feed-forward ANN is constructed to find a feasible range of ALD operating conditions accounting for industrial considerations, and a Bayesian Regularized ANN is constructed to implement the cycle-to-cycle optimization of ALD cycle time. Extensive simulation results demonstrate the effectiveness of the proposed approaches. The kMC models successfully achieves a growth per cycle (GPC) of 1.8 A per cycle, which is in the range of reported experimental values. The ANN models accurately predict deposition time to steady-state from the given operating condition input, and the cycle time optimization using BRANN model reduces the conventional BTBAS cycle time by 60%. After developing an efficient simulation framework, a more detailed study on the optimal operation policy is performed using a multiscale data-driven model. The multiscale data-driven model captures the macroscopic process domain dynamics with a linear parameter varying model, and characterizes the microscopic domain film growth dynamics with a feed-forward artificial neural network (ANN) model. The multiscale data-driven model predicts the transient deposition rate from the following four key process operating parameters that can be manipulated, measured or estimated by process engineers: precursor feed flow rate, operating pressure, surface heating, and transient film coverage. Our results demonstrate that the multiscale data-driven model can efficiently characterize the transient input-output relationship for the SiO2 thermal ALD process using Bis(tertiary-butylamino)silane (BTBAS) as the Si precursor. The multiscale data-driven model successfully reduces the computational time from 0.6 - 1.2 hr for each time step, which is required for the first-principles based multiscale computational fluid dynamics (CFD) model, to less than 0.1 s, making its real-time usage feasible. The developed data-driven modeling methodology can be further generalized and used for other thermal ALD or similar deposition systems, which will greatly enhance the feasibility of industrial manufacturing processes. For PEALD, a similar methodology is adopted. First, an accurate, yet efficient kinetic Monte Carlo (kMC) model and an associated machine learning (ML) analysis are proposed to capture the surface deposition mechanism of the HfO2 thin-film PEALD using Tetrakis-dimethylamino-Hafnium (TDMAHf) and oxygen plasma. Density Functional Theory (DFT) calculations are performed to obtain the key kinetic parameters and the structural details. After the model is validated by experimental data, a database is generated to explore a variety of precursor partial pressure and substrate temperature combinations using the kMC algorithm. A feed-forward Bayesian regularized artificial neural network (BRANN) is then constructed to characterize the input-output relationship and to investigate the optimal operating condition. Next, based on an associated work on a comprehensive 3D multiscale computational fluid dynamics (CFD) model for the PEALD process, a 2D axisymmetric reduction of the previous 3D model of PEALD reactors with and without the showerhead design has been adopted to enhance the computational efficiency. Using the derived 2D CFD model, a data-driven model is constructed based on a recurrent neural network (RNN) for process characterization. The developed integrated data-driven model is demonstrated to accurately characterize the key aspects of the deposition process as well as the gas-phase transport profile while maintaining computational efficiency. The derived data-driven model is further validated with the results from a full 3D multiscale CFD model to evaluate model discrepancy. Using the data-driven model, an operational strategy database is generated, from which the optimal operating conditions can be determined for the deposition of HfO2 thin-film based on an elementary cost analysis.
Dissertation Abstracts International
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Author :
language : en
Publisher:
Release Date : 2008
Dissertation Abstracts International written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Dissertations, Academic categories.
Applied Science Technology Index
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Author :
language : en
Publisher:
Release Date : 1997
Applied Science Technology Index written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Electronic journals categories.
Research Centers Directory
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Author :
language : en
Publisher:
Release Date : 2010
Research Centers Directory written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Associations, institutions, etc categories.
Research institutes, foundations, centers, bureaus, laboratories, experiment stations, and other similar nonprofit facilities, organizations, and activities in the United States and Canada. Entry gives identifying and descriptive information of staff and work. Institutional, research centers, and subject indexes. 5th ed., 5491 entries; 6th ed., 6268 entries.
Process Modelling And Simulation
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Author : César de Prada
language : en
Publisher: MDPI
Release Date : 2019-09-23
Process Modelling And Simulation written by César de Prada and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-23 with Technology & Engineering categories.
Since process models are nowadays ubiquitous in many applications, the challenges and alternatives related to their development, validation, and efficient use have become more apparent. In addition, the massive amounts of both offline and online data available today open the door for new applications and solutions. However, transforming data into useful models and information in the context of the process industry or of bio-systems requires specific approaches and considerations such as new modelling methodologies incorporating the complex, stochastic, hybrid and distributed nature of many processes in particular. The same can be said about the tools and software environments used to describe, code, and solve such models for their further exploitation. Going well beyond mere simulation tools, these advanced tools offer a software suite built around the models, facilitating tasks such as experiment design, parameter estimation, model initialization, validation, analysis, size reduction, discretization, optimization, distributed computation, co-simulation, etc. This Special Issue collects novel developments in these topics in order to address the challenges brought by the use of models in their different facets, and to reflect state of the art developments in methods, tools and industrial applications.
Atomic Layer Deposition For Semiconductors
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Author : Cheol Seong Hwang
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-10-18
Atomic Layer Deposition For Semiconductors written by Cheol Seong Hwang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-18 with Science categories.
Offering thorough coverage of atomic layer deposition (ALD), this book moves from basic chemistry of ALD and modeling of processes to examine ALD in memory, logic devices and machines. Reviews history, operating principles and ALD processes for each device.
Machine Learning Based Modeling And Operation Of Plasma Enhanced Atomic Layer Deposition Of Hafnium Oxide Thin Films
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Author : Ho Yeon Chung
language : en
Publisher:
Release Date : 2020
Machine Learning Based Modeling And Operation Of Plasma Enhanced Atomic Layer Deposition Of Hafnium Oxide Thin Films written by Ho Yeon Chung and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Plasma-enhanced atomic layer deposition (PEALD) has demonstrated its superiority at coatingultra-conformal high dielectric thin-films, which are essential to the fin field-effect transistors (FinFETs) as well as the advanced 3D V-NAND (vertical Not-AND) flash memory cells. Despite the growing research interest, the exploration of the optimal operation policies for PEALD remains a complicated and expensive task. Our previous work has constructed a comprehensive 3D multiscale computational fluid dynamics (CFD) model for the PEALD process and demonstrated its potential to enhance the understanding of the process. Nevertheless, the limitation of computational resources and the relatively long computation time restrict the efficient exploration of the operating space and the optimal operating strategy. Thus, in this work, we apply a 2D axisymmetric reduction of the previous 3D model of PEALD reactors with and without the showerhead design. Furthermore, a data-driven model is derived based on a recurrent neural network (RNN) for process characterization. The developed integrated data-driven model is demonstrated to accurately characterize the key aspects of the deposition process as well as the gas-phase transport profile while maintaining computational efficiency. The derived data-driven model is further validated with the results from a full 3D multiscale CFD model to evaluate model discrepancy. Using the data-driven model, an operational strategy database is generated, from which the optimal operating conditions can be determined for the deposition of HfO2 thin-film based on an elementary cost analysis.
Computational Chemistry
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Author : Errol G. Lewars
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-08
Computational Chemistry written by Errol G. Lewars and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-08 with Science categories.
Computational chemistry has become extremely important in the last decade, being widely used in academic and industrial research. Yet there have been few books designed to teach the subject to nonspecialists. Computational Chemistry: Introduction to the Theory and Applications of Molecular and Quantum Mechanics is an invaluable tool for teaching and researchers alike. The book provides an overview of the field, explains the basic underlying theory at a meaningful level that is not beyond beginners, and it gives numerous comparisons of different methods with one another and with experiment. The following concepts are illustrated and their possibilities and limitations are given: - potential energy surfaces; - simple and extended Hückel methods; - ab initio, AM1 and related semiempirical methods; - density functional theory (DFT). Topics are placed in a historical context, adding interest to them and removing much of their apparently arbitrary aspect. The large number of references, to all significant topics mentioned, should make this book useful not only to undergraduates but also to graduate students and academic and industrial researchers.