Microscopic modeling and optimal operation of plasma enhanced atomic layer deposition

Abstract Plasma enhanced atomic layer deposition (PEALD) is one of the most widely adopted deposition methods used in the semiconductor industry. It is chosen largely due to its superior ability to produce ultra-thin high-k dielectric films, which are needed for the further miniaturization of microelectronic devices with the pace of Moore's Law. In contrast to the traditional thermal atomic layer deposition (ALD) method, PEALD allows for high deposition growth per cycle (GPC) under low operating temperature with the help of high energy plasma species. Despite the experimental effort in finding new precursors and plasmas, the detailed surface structures and reaction mechanisms in various PEALD processes remain hard to understand because of the limitation of current in-situ monitoring techniques and the deficiency of the first-principles based analysis. Therefore, in this work, an accurate, yet efficient kinetic Monte Carlo (kMC) model and an associated machine learning (ML) analysis are proposed to capture the surface deposition mechanism and to propose optimal operating conditions 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, subsequently employed in the kMC model. After the kMC 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 conditions.

[1]  A. Brand,et al.  Semiconductor Logic Technology Innovation to Achieve Sub-10 nm Manufacturing , 2013, IEEE Journal of the Electron Devices Society.

[2]  P. Wild,et al.  Long period gratings coated with hafnium oxide by plasma-enhanced atomic layer deposition for refractive index measurements. , 2016, Optics express.

[3]  P. Christofides,et al.  Plasma enhanced chemical vapor deposition: Modeling and control , 1999 .

[4]  J. Dumesic,et al.  Kinetics of heterogeneous catalytic reactions: Analysis of reaction schemes , 2001 .

[5]  Hcm Harm Knoops,et al.  Conformality of Plasma-Assisted ALD: Physical Processes and Modeling , 2010 .

[6]  Sumit Agarwal,et al.  Surface reaction mechanisms during ozone and oxygen plasma assisted atomic layer deposition of aluminum oxide. , 2010, Langmuir : the ACS journal of surfaces and colloids.

[7]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[8]  T. Schram,et al.  Development of ALD HfZrO x with TDEAH/TDEAZ and H2O , 2011 .

[9]  Thomas E. Seidel,et al.  Atomic layer deposition of hafnium oxide thin films from tetrakis (dimethylamino) hafnium (TDMAH) and ozone , 2003 .

[10]  Yangdo Kim,et al.  Plasma-Enhanced Atomic-Layer Deposition of a HfO2 Gate Dielectric , 2004 .

[11]  P. Christofides,et al.  Dependence of film surface roughness and slope on surface migration and lattice size in thin film deposition processes , 2010 .

[12]  Luis A. Ricardez-Sandoval,et al.  Nonlinear model predictive control of a multiscale thin film deposition process using artificial neural networks , 2019, Chemical Engineering Science.

[13]  R. Adomaitis,et al.  Modeling alumina atomic layer deposition reaction kinetics during the trimethylaluminum exposure , 2013, Theoretical Chemistry Accounts.

[14]  F. Jensen.  Introduction to Computational Chemistry , 1998 .

[15]  J. Roh,et al.  Interfacial layer properties of HfO2 films formed by plasma-enhanced atomic layer deposition on silicon , 2006 .

[16]  Panagiotis D. Christofides,et al.  Microscopic modeling and optimal operation of thermal atomic layer deposition , 2019, Chemical Engineering Research and Design.

[17]  Krishna C. Saraswat,et al.  Monte Carlo low pressure deposition profile simulations , 1991 .

[18]  W. R. Wadt,et al.  Ab initio effective core potentials for molecular calculations. Potentials for K to Au including the outermost core orbitals , 1985 .

[19]  Jane P. Chang,et al.  Progress and prospects in nanoscale dry processes: How can we control atomic layer reactions? , 2017 .

[20]  Youngdo Won,et al.  The reaction pathways of the oxygen plasma pulse in the hafnium oxide atomic layer deposition process , 2008 .

[21]  Shabnam Rasoulian,et al.  Robust multivariable estimation and control in an epitaxial thin film growth process under uncertainty , 2015 .

[22]  Qiang Zhu,et al.  Evolutionary search for new high-k dielectric materials: methodology and applications to hafnia-based oxides. , 2014, Acta crystallographica. Section C, Structural chemistry.

[23]  T. Gilbert.  Hohenberg--Kohn theorem for nonlocal external potentials , 1975 .

[24]  J. Yun,et al.  The Effects of Thermal Decomposition of Tetrakis-ethylmethylaminohafnium (TEMAHf) Precursors on HfO2 Film Growth using Atomic Layer Deposition , 2016 .

[25]  Shigeru Kubota,et al.  Infrared study on room-temperature atomic layer deposition of TiO2 using tetrakis(dimethylamino)titanium and remote-plasma-excited water vapor , 2014 .

[26]  G. Ghibaudo,et al.  Review on high-k dielectrics reliability issues , 2005, IEEE Transactions on Device and Materials Reliability.

[27]  R. Adomaitis,et al.  Modeling ALD Surface Reaction and Process Dynamics using Absolute Reaction Rate Theory , 2013 .

[28]  Annemie Bogaerts,et al.  Computer simulations of an oxygen inductively coupled plasma used for plasma-assisted atomic layer deposition , 2011 .

[29]  Youngdo Won,et al.  Initial reaction of hafnium oxide deposited by remote plasma atomic layer deposition method , 2005 .

[30]  Panagiotis D. Christofides,et al.  Integrating Feedback Control and Run-to-Run Control in Multi-Wafer Thermal Atomic Layer Deposition of Thin Films , 2019, Processes.

[31]  Heidi Abrahamse,et al.  A novel approach to low-temperature synthesis of cubic HfO2 nanostructures and their cytotoxicity , 2017, Scientific Reports.

[32]  Mohamed A. Zohdy,et al.  Comparative Performance of Several Recent Supervised Learning Algorithms , 2018 .

[33]  W. R. Wadt,et al.  Ab initio effective core potentials for molecular calculations , 1984 .

[34]  Roy G. Gordon,et al.  ALD of Hafnium Oxide Thin Films from Tetrakis(ethylmethylamino)hafnium and Ozone , 2005 .

[35]  Daniel Svozil,et al.  Introduction to multi-layer feed-forward neural networks , 1997 .

[36]  C. Peden,et al.  Evidence for oxygen adatoms on TiO2(110) resulting from O2 dissociation at vacancy sites , 1998 .

[37]  F. Prinz,et al.  Atomic layer deposition by reaction of molecular oxygen with tetrakisdimethylamido-metal precursors , 2016 .

[38]  Sun Jin Yun,et al.  PEALD of Zirconium Oxide Using Tetrakis(ethylmethylamino)zirconium and Oxygen , 2004 .

[39]  Panagiotis D. Christofides,et al.  Multiscale computational fluid dynamics modeling of thermal atomic layer deposition with application to chamber design , 2019, Chemical Engineering Research and Design.

[40]  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.

[41]  Sbs Stephan Heil,et al.  Deposition of TiN and HfO2 in a commercial 200 mm remote plasma atomic layer deposition reactor , 2007 .

[42]  Mahdi Shirazi,et al.  Atomistic kinetic Monte Carlo study of atomic layer deposition derived from density functional theory , 2014, J. Comput. Chem..

[43]  David J. Srolovitz,et al.  Kinetic Monte Carlo Simulation of Chemical Vapor Deposition , 2002 .

[44]  Panagiotis D. Christofides,et al.  Machine learning-based modeling and operation for ALD of SiO2 thin-films using data from a multiscale CFD simulation , 2019, Chemical Engineering Research and Design.

[45]  Yu Liu,et al.  Development of 3D Polymer DFT and Its Application to Molecular Transport through a Surfactant-Covered Interface , 2018 .

[46]  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 .

[47]  C. Kleijn,et al.  A General Correction to Surface Reaction Models Based on Reactive Sticking Coefficients , 2007 .

[48]  E. Baerends,et al.  Exchange and correlation energy in density functional theory. Comparison of accurate DFT quantities with traditional Hartree-Fock based ones and generalized gradient approximations for the molecules Li2, N2, F2. , 1997 .

[49]  Martin Oettel,et al.  Experimental and simulation approach for process optimization of atomic layer deposited thin films in high aspect ratio 3D structures , 2017 .

[50]  Panagiotis D. Christofides,et al.  Multiscale three-dimensional CFD modeling for PECVD of amorphous silicon thin films , 2018, Comput. Chem. Eng..

[51]  Jane P. Chang,et al.  Plasma enhanced atomic layer deposition of HfO2 and ZrO2 high-k thin films , 2005 .

[52]  J. J. Moré,et al.  Levenberg--Marquardt algorithm: implementation and theory , 1977 .

[53]  W. R. Wadt,et al.  Ab initio effective core potentials for molecular calculations. Potentials for main group elements Na to Bi , 1985 .

[54]  N. J. Ianno,et al.  Ellipsometry study of non-uniform lateral growth of ZnO thin films , 1993 .

[55]  S. George.  Atomic layer deposition: an overview. , 2010, Chemical reviews.

[56]  Seongjun Park,et al.  Thickness scaling of atomic-layer-deposited HfO2 films and their application to wafer-scale graphene tunnelling transistors , 2016, Scientific Reports.

[57]  S. Rossnagel,et al.  Plasma Modeling of a PEALD System for the Deposition of TiO2 and HfO2 , 2009 .

[58]  James C. Greer,et al.  Simulating the atomic layer deposition of alumina from first principles , 2004 .

[59]  Mikko Ritala,et al.  Atomic Layer Deposition of Titanium Oxide from TiI4 and H2O2 , 2000 .