Machine Learning Based Modeling And Operation Of Plasma Enhanced Atomic Layer Deposition Of Hafnium Oxide Thin Films
DOWNLOAD
Download Machine Learning Based Modeling And Operation Of Plasma Enhanced Atomic Layer Deposition Of Hafnium Oxide Thin Films PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Machine Learning Based Modeling And Operation Of Plasma Enhanced Atomic Layer Deposition Of Hafnium Oxide Thin Films book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Machine Learning Based Modelling In Atomic Layer Deposition Processes
DOWNLOAD
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.
Machine Learning Based Modeling And Operation Of Plasma Enhanced Atomic Layer Deposition Of Hafnium Oxide Thin Films
DOWNLOAD
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.
Microscopic Modeling Machine Learning Based Modeling And Optimal Operation Of Thermal And Plasma Atomic Layer Deposition
DOWNLOAD
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.
Ferroelectricity In Doped Hafnium Oxide
DOWNLOAD
Author : Uwe Schroeder
language : en
Publisher: Woodhead Publishing
Release Date : 2019-03-27
Ferroelectricity In Doped Hafnium Oxide written by Uwe Schroeder and has been published by Woodhead Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-27 with Technology & Engineering categories.
Ferroelectricity in Doped Hafnium Oxide: Materials, Properties and Devices covers all aspects relating to the structural and electrical properties of HfO2 and its implementation into semiconductor devices, including a comparison to standard ferroelectric materials. The ferroelectric and field-induced ferroelectric properties of HfO2-based films are considered promising for various applications, including non-volatile memories, negative capacitance field-effect-transistors, energy storage, harvesting, and solid-state cooling. Fundamentals of ferroelectric and piezoelectric properties, HfO2 processes, and the impact of dopants on ferroelectric properties are also extensively discussed in the book, along with phase transition, switching kinetics, epitaxial growth, thickness scaling, and more. Additional chapters consider the modeling of ferroelectric phase transformation, structural characterization, and the differences and similarities between HFO2 and standard ferroelectric materials. Finally, HfO2 based devices are summarized. - Explores all aspects of the structural and electrical properties of HfO2, including processes, modelling and implementation into semiconductor devices - Considers potential applications including FeCaps, FeFETs, NCFETs, FTJs and more - Provides comparison of an emerging ferroelectric material to conventional ferroelectric materials with insights to the problems of downscaling that conventional ferroelectrics face
Process Systems Analysis And Control
DOWNLOAD
Author : Steven E. LeBlanc
language : en
Publisher:
Release Date : 2013
Process Systems Analysis And Control written by Steven E. LeBlanc and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Chemical process control categories.
Additive Manufacturing Technologies
DOWNLOAD
Author : Ian Gibson
language : en
Publisher: Springer Nature
Release Date : 2020-11-10
Additive Manufacturing Technologies written by Ian Gibson and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-10 with Technology & Engineering categories.
This textbook covers in detail digitally-driven methods for adding materials together to form parts. A conceptual overview of additive manufacturing is given, beginning with the fundamentals so that readers can get up to speed quickly. Well-established and emerging applications such as rapid prototyping, micro-scale manufacturing, medical applications, aerospace manufacturing, rapid tooling and direct digital manufacturing are also discussed. This book provides a comprehensive overview of additive manufacturing technologies as well as relevant supporting technologies such as software systems, vacuum casting, investment casting, plating, infiltration and other systems. Reflects recent developments and trends and adheres to the ASTM, SI and other standards; Includes chapters on topics that span the entire AM value chain, including process selection, software, post-processing, industrial drivers for AM, and more; Provides a broad range of technical questions to ensure comprehensive understanding of the concepts covered.
Carbon Nanotube Devices
DOWNLOAD
Author :
language : en
Publisher: John Wiley & Sons
Release Date : 2008-05-05
Carbon Nanotube Devices written by and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-05-05 with Technology & Engineering categories.
Following on from the first AMN volume, this handy reference and textbook examines the topic of nanosystem design in further detail. It explains the physical and chemical basics behind the design and fabrication of nanodevices, covering all important, recent advances in the field, while introducing nanosystems to less experienced readers. The result is an important source for a fast, accurate overview of the state of the art of nanosystem realization, summarizing further important literature.
Atomic Layer Deposition For Semiconductors
DOWNLOAD
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.
Handbook Of Neuroengineering
DOWNLOAD
Author : Nitish V. Thakor
language : en
Publisher: Springer Nature
Release Date : 2023-02-02
Handbook Of Neuroengineering written by Nitish V. Thakor and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-02 with Technology & Engineering categories.
This Handbook serves as an authoritative reference book in the field of Neuroengineering. Neuroengineering is a very exciting field that is rapidly getting established as core subject matter for research and education. The Neuroengineering field has also produced an impressive array of industry products and clinical applications. It also serves as a reference book for graduate students, research scholars and teachers. Selected sections or a compendium of chapters may be used as “reference book” for a one or two semester graduate course in Biomedical Engineering. Some academicians will construct a “textbook” out of selected sections or chapters. The Handbook is also meant as a state-of-the-art volume for researchers. Due to its comprehensive coverage, researchers in one field covered by a certain section of the Handbook would find other sections valuable sources of cross-reference for information and fertilization of interdisciplinary ideas. Industry researchers as well as clinicians using neurotechnologies will find the Handbook a single source for foundation and state-of-the-art applications in the field of Neuroengineering. Regulatory agencies, entrepreneurs, investors and legal experts can use the Handbook as a reference for their professional work as well.
The Dynamical Theory Of Gases
DOWNLOAD
Author : James Jeans
language : en
Publisher: Cambridge University Press
Release Date : 2009-01-18
The Dynamical Theory Of Gases written by James Jeans and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-01-18 with Science categories.
Jeans's primary aim with the first edition of his book, originally published in 1904, was to 'develop the theory of gases upon as exact a mathematical basis as possible'. Twenty years later and those theories were being revolutionised by Quantum Theory. In this fourth edition, Jeans does not attempt to avoid the discoveries of this new science, but rather exposes the many difficulties that classical theory was experiencing, and how those problems disappeared with Quantum Theory. This edition therefore offers a fascinating insight into a field of physics in transition between two great models of physical science.