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.

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.

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.

Facilitated by the increasing importance and demand of thin-film materials, plasma-enhanced atomic layer deposition (PEALD) has gained tremendous industrial interest as it offers a way to efficiently deposit thin-films with ultra-high conformity. Despite the variety of PEALD processes, there lacks a fundamental and general methodology to understand, characterize and control a realistic PEALD process system. To fully understand the PEALD process, a series of studies have been carried out in our previous work. First, a kinetic Monte-Carlo (kMC)-based microscopic model that describes the surface dynamics was implemented and a multiscale CFD model was developed to characterize the PEALD process. Additionally, a corresponding multiscale data-driven model was derived to efficiently explore the optimal operating condition in the process domain based-on an elementary cost-analysis. The successful development of the aforementioned models enables the further study into the process control of PEALD where disturbances in operating conditions are present. In this work, an integrated control scheme using a proportional-integral (PI) controller and a run-to-run (R2R) controller is proposed and implemented. Using the developed multiscale CFD model, the PEALD process under typical disturbances is simulated, and the controllers are applied in the process domain. The result demonstrates the successful mitigation of disturbances in operating pressure, inlet molar fraction and gas feeder temperature under the combined effort of both controllers.

Facilitated by the increasing importance and demand of semiconductors for the smartphoneand even the automobile industry, thermal atomic layer deposition (ALD) has gained tremendous industrial interest as it offers a way to efficiently deposit thin-films with ultra-high conformity. It is chosen largely due to its superior ability to deliver ultra-conformal dielectric thin-films with high aspect-ratio surface structures, which are encountered more and more often in the novel design of metal-oxide-semiconductor field-effect transistors (MOSFETs) in the NAND (Not-And)-type flash memory devices. Based on the traditional thermal ALD method, the plasma enhanced atomic layer deposition (PEALD) allows for lower operating temperature and speeds up the deposition process with the involvement of plasma species. Despite the popularity of these two methods, the development of their operation policies remains a complicated and expensive task, which motivates the construction of an accurate and comprehensive simulation model. A series of studies have been carried out to elucidate the mechanisms and the conceptof the PEALD process. In particular, process characterization focuses on the development of a first-principles-based three-dimensional, multiscale computational fluid dynamics (CFD) model, together with reactor geometry optimizations, of SiO2 thinfilm thermal atomic layer deposition (ALD) using bis(tertiary-butylamino)silane (BTBAS) and ozone as precursors. Also, a comprehensive multiscale computational fluid dynamics (CFD) model incorporating the plasma generation chamber is used in the deposition of HfO2 thin-films utilizing tetrakis(dimethylamido) hafnium (TDMAHf) and O2 plasma as precursors. Despite the great deal of research effort, ALD and PEALD processes have not been fullycharacterized from the view point of process control. This study aims to use previously developed multiscale CFD simulation model to design and evaluate an optimized control scheme to deal with industrially-relevant disturbances. Specifically, an integrated control scheme using a proportional-integral (PI) controller and a run-to-run (R2R) controller is proposed and evaluated to ensure the deposition of high-quality conformal thin-films. The ALD and PEALD processes under typical disturbances are simulated using the multiscale CFD model, and the integrated controllers are applied in the process domain. Using the controller parameters determined from the open-loop results, the developed integrated PI-R2R controller successfully mitigates the disturbances in the reactor with the combined effort of both controllers.

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.