Microscopic modeling and optimal operation of thermal atomic layer deposition
暂无分享,去创建一个
Panagiotis D. Christofides | Yangyao Ding | Zhe Wu | Anh Tran | Yichi Zhang | P. Christofides | Zhe Wu | Anh Tran | Keegan Kim | Yichi Zhang | Yangyao Ding | K. Kim
[1] Prashant Mhaskar,et al. Modeling and Control of Batch Processes: Theory and Applications , 2018 .
[2] Luis A. Ricardez-Sandoval,et al. Optimization and control of a thin film growth process: A hybrid first principles/artificial neural network based multiscale modelling approach , 2018, Comput. Chem. Eng..
[3] Prashanth Siddhamshetty,et al. Approximate Dynamic Programming Based Control of Proppant Concentration in Hydraulic Fracturing , 2018, Mathematics.
[4] Panagiotis D. Christofides,et al. Multiscale three-dimensional CFD modeling for PECVD of amorphous silicon thin films , 2018, Comput. Chem. Eng..
[5] Luis A. Ricardez-Sandoval,et al. Robust dynamic optimization in heterogeneous multiscale catalytic flow reactors using polynomial chaos expansion , 2017 .
[6] Grigoriy Kimaev,et al. A comparison of efficient uncertainty quantification techniques for stochastic multiscale systems , 2017 .
[7] Jane P. Chang,et al. Progress and prospects in nanoscale dry processes: How can we control atomic layer reactions? , 2017 .
[8] J. Bartha,et al. Temperature dependence of the sticking coefficients of bis-diethyl aminosilane and trimethylaluminum in atomic layer deposition , 2017 .
[9] Martin Oettel,et al. Experimental and simulation approach for process optimization of atomic layer deposited thin films in high aspect ratio 3D structures , 2017 .
[10] N. Dasgupta,et al. Atomic Layer Deposition for Energy and Environmental Applications , 2016 .
[11] Se-Kyu Oh,et al. Iterative learning model predictive control for constrained multivariable control of batch processes , 2016, Comput. Chem. Eng..
[12] Sebastian Ruder,et al. An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.
[13] P. Jha,et al. First-principles study of water adsorption on α-SiO2 [110] surface , 2016 .
[14] Shabnam Rasoulian,et al. Stochastic nonlinear model predictive control applied to a thin film deposition process under uncertainty , 2016 .
[15] Shabnam Rasoulian,et al. A robust nonlinear model predictive controller for a multiscale thin film deposition process , 2015 .
[16] Shabnam Rasoulian,et al. Robust multivariable estimation and control in an epitaxial thin film growth process under uncertainty , 2015 .
[17] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[18] Panagiotis D. Christofides,et al. A method for handling batch-to-batch parametric drift using moving horizon estimation: Application to run-to-run MPC of batch crystallization , 2015 .
[19] Panagiotis D. Christofides,et al. Run-to-Run-Based Model Predictive Control of Protein Crystal Shape in Batch Crystallization , 2015 .
[20] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[21] Shabnam Rasoulian,et al. Uncertainty analysis and robust optimization of multiscale process systems with application to epitaxial thin film growth , 2014 .
[22] C. Murray,et al. Effect of reaction mechanism on precursor exposure time in atomic layer deposition of silicon oxide and silicon nitride. , 2014, ACS applied materials & interfaces.
[23] Helena Ronkainen,et al. Thermal and plasma enhanced atomic layer deposition of SiO2 using commercial silicon precursors , 2014 .
[24] Mahdi Shirazi,et al. Atomistic kinetic Monte Carlo study of atomic layer deposition derived from density functional theory , 2014, J. Comput. Chem..
[25] Hansong Cheng,et al. First-Principles Study of a Full Cycle of Atomic Layer Deposition of SiO2 Thin Films with Di(sec-butylamino)silane and Ozone , 2013 .
[26] A. Brand,et al. Semiconductor Logic Technology Innovation to Achieve Sub-10 nm Manufacturing , 2013, IEEE Journal of the Electron Devices Society.
[27] Arthur D. Sherman,et al. Atomic Layer Deposition: Principles, Characteristics, and Nanotechnology Applications , 2013 .
[28] Panagiotis D. Christofides,et al. Crystal shape modeling and control in protein crystal growth , 2013 .
[29] Yeong-Cheol Kim,et al. Adsorption and surface reaction of bis-diethylaminosilane as a Si precursor on an OH-terminated Si (0 0 1) surface , 2012 .
[30] Hansong Cheng,et al. On the Mechanisms of SiO2 Thin-Film Growth by the Full Atomic Layer Deposition Process Using Bis(t-butylamino)silane on the Hydroxylated SiO2(001) Surface , 2012 .
[31] Wmm Erwin Kessels,et al. Plasma-Assisted ALD for the Conformal Deposition of SiO2: Process, Material and Electronic Properties , 2012 .
[32] Fujio Izumi,et al. VESTA 3 for three-dimensional visualization of crystal, volumetric and morphology data , 2011 .
[33] P. Christofides,et al. Dynamics and Lattice-Size Dependence of Surface Mean Slope in Thin-Film Deposition , 2011 .
[34] Kirk Scott Cuthill,et al. Impact of Aminosilane Precursor Structure on Silicon Oxides by Atomic Layer Deposition , 2011 .
[35] P. Christofides,et al. Dependence of film surface roughness and slope on surface migration and lattice size in thin film deposition processes , 2010 .
[36] Hcm Harm Knoops,et al. Conformality of Plasma-Assisted ALD: Physical Processes and Modeling , 2010 .
[37] Xiao Hu,et al. Template‐Directed Liquid ALD Growth of TiO2 Nanotube Arrays: Properties and Potential in Photovoltaic Devices , 2010 .
[38] N. Castin,et al. Calculation of proper energy barriers for atomistic kinetic Monte Carlo simulations on rigid lattice with chemical and strain field long-range effects using artificial neural networks. , 2010, The Journal of chemical physics.
[39] S. George. Atomic layer deposition: an overview. , 2010, Chemical reviews.
[40] Francis J. Doyle,et al. Survey on iterative learning control, repetitive control, and run-to-run control , 2009 .
[41] Chenggang Zhou,et al. On the Dissociative Chemisorption of Tris(dimethylamino)silane on Hydroxylated SiO2(001) Surface , 2009 .
[42] Dave Winkler,et al. Bayesian Regularization of Neural Networks , 2009, Artificial Neural Networks.
[43] A. Dkhissi,et al. Multiscale Modeling of the Atomic Layer Deposition of HfO2 Thin Film Grown on Silicon: How to Deal with a Kinetic Monte Carlo Procedure. , 2008, Journal of chemical theory and computation.
[44] Jane P. Chang,et al. Electrical performance of Al2O3 gate dielectric films deposited by atomic layer deposition on 4H-SiC , 2007 .
[45] Yasuo Kimura,et al. Infrared Study of Tris(dimethylamino)silane Adsorption and Ozone Irradiation on Si(100) Surfaces for ALD of SiO2 , 2007 .
[46] Roberto P. Domingos,et al. Artificial intelligence applied to atomistic kinetic Monte Carlo simulations in Fe–Cu alloys , 2007 .
[47] Satoshi Kamiyama,et al. Comparison between SiO2 films deposited by atomic layer deposition with SiH2[N(CH3)2]2 and SiH[N(CH3)2]3 precursors , 2006 .
[48] Antonios Armaou,et al. Control and optimization of multiscale process systems , 2006, Comput. Chem. Eng..
[49] Jay H. Lee,et al. Approximate dynamic programming-based approaches for input-output data-driven control of nonlinear processes , 2005, Autom..
[50] Panagiotis D. Christofides,et al. Feedback control of surface roughness of GaAs [001] thin films using kinetic Monte-Carlo models , 2004, Proceedings of the 2004 American Control Conference.
[51] James C. Greer,et al. Simulating the atomic layer deposition of alumina from first principles , 2004 .
[52] A. Kersch,et al. A model for Al2O3 ALD conformity and deposition rate from oxygen precursor reactivity , 2003 .
[53] T. Nishiguchi,et al. High-quality SiO2 film formation by highly concentrated ozone gas at below 600°C , 2002 .
[54] David J. Srolovitz,et al. Kinetic Monte Carlo Simulation of Chemical Vapor Deposition , 2002 .
[55] J. Dumesic,et al. Kinetics of heterogeneous catalytic reactions: Analysis of reaction schemes , 2001 .
[56] Daniel Svozil,et al. Introduction to multi-layer feed-forward neural networks , 1997 .
[57] Steven M. George,et al. Surface Chemistry for Atomic Layer Growth , 1996 .
[58] B. El-Kareh. Fundamentals of Semiconductor Processing Technology , 1994 .
[59] A. Becke. Density-functional thermochemistry. III. The role of exact exchange , 1993 .
[60] David J. C. MacKay,et al. Bayesian Interpolation , 1992, Neural Computation.
[61] Krishna C. Saraswat,et al. Monte Carlo low pressure deposition profile simulations , 1991 .
[62] Masato Ikegawa,et al. Deposition Profile Simulation Using the Direct Simulation Monte Carlo Method , 1989 .
[63] Parr,et al. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. , 1988, Physical review. B, Condensed matter.
[64] J. J. Moré,et al. Levenberg--Marquardt algorithm: implementation and theory , 1977 .