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• Kinetic Monte-Carlo modeling of PEALD. • Calculation of kMC model parameters via DFT. • Comparison with experimental data. • Neural network modeling for determining optimal operating conditions. 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 HfO 2 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. [ABSTRACT FROM AUTHOR]

Electroreduction of CO2 to C2+ products provides a promising strategy for reaching the goal of carbon neutrality. However, achieving high selectivity of C2+ products at high current density remains a challenge. In this work, we designed and prepared a multi‐sites catalyst, in which Pd was atomically dispersed in Cu (Pd−Cu). It was found that the Pd−Cu catalyst had excellent performance for producing C2+ products from CO2 electroreduction. The Faradaic efficiency (FE) of C2+ products could be maintained at approximately 80.8 %, even at a high current density of 0.8 A cm−2 for at least 20 hours. In addition, the FE of C2+ products was above 70 % at 1.4 A cm−2. Experiments and density functional theory (DFT) calculations revealed that the catalyst had three distinct catalytic sites. These three active sites allowed for efficient conversion of CO2, water dissociation, and CO conversion, ultimately leading to high yields of C2+ products. [ABSTRACT FROM AUTHOR]

Electroreduction of CO2 to C2+ products provides a promising strategy for reaching the goal of carbon neutrality. However, achieving high selectivity of C2+ products at high current density remains a challenge. In this work, we designed and prepared a multi‐sites catalyst, in which Pd was atomically dispersed in Cu (Pd−Cu). It was found that the Pd−Cu catalyst had excellent performance for producing C2+ products from CO2 electroreduction. The Faradaic efficiency (FE) of C2+ products could be maintained at approximately 80.8 %, even at a high current density of 0.8 A cm−2 for at least 20 hours. In addition, the FE of C2+ products was above 70 % at 1.4 A cm−2. Experiments and density functional theory (DFT) calculations revealed that the catalyst had three distinct catalytic sites. These three active sites allowed for efficient conversion of CO2, water dissociation, and CO conversion, ultimately leading to high yields of C2+ products. [ABSTRACT FROM AUTHOR]

• DFT-based calculation of microscopic model parameters. • Microscopic modeling of ALD. • Understanding of operational limits of ALD. • Cycle time optimization of ALD using ANN. This work develops a comprehensive framework for first-principles-based microscopic modeling, data-driven input/output modeling and optimal operation of thermal atomic layer deposition (ALD) of SiO 2 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 dense 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 Å 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%. [ABSTRACT FROM AUTHOR]

Aluminum (Al), gallium (Ga) and indium (In) are three essential elements in group IIIA of the periodic tables, which exhibit similar chemical behaviors in aqueous solutions. A novel fluorescent probe 4-hydrazino-7-nitrobenzofurazan-2-hydroxy-1-naphthaldehyde (NBD-hnap) by a simple conjugation of NBD and hnap molecules is found to detect the IIIA group ions based the 'turn-on' fluorescent. Here, the sensing mechanisms of probe NBD-hnap to detect IIIA group ions are explored by density functional theory (DFT) and time-dependent DFT (TDDFT) theoretical methods. In aqueous solution, the NBD-hnap has two absorption peaks, and the fluorescence intensity is almost zero during the emission process. After adding IIIA group ions, the absorption spectrum was blue shifted and showed strongly red fluorescence signal. In our calculation, the presence of the C=N bond may induce a twisted intramolecular charge transfer (TICT) state. Thus, the fluorescence quenching of NBD-hnap is caused by TICT process. When NBD-hnap is coordination with metal ions, the TICT state is eliminated. [ABSTRACT FROM AUTHOR]

Plasma-enhanced atomic layer deposition (PEALD) is one of the most widely adopted deposition methods in the semiconductor industry. 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. Compared with the traditional thermal ALD method, PEALD allows for lower operating temperature and speeds up the deposition process with the involvement of plasma species. Despite its popularity, the development of PEALD operation policies remains a complicated and expensive task, which motivates the construction of an accurate and comprehensive simulation model. While existing models have described the individual or partially coupled domains, none of these models has captured all three domains in the PEALD process: surface reaction, macroscopic gas transport, and plasma generation. In this work, a comprehensive multiscale computational fluid dynamics (CFD) model is developed for a remote PEALD reactor used in the deposition of HfO 2 thin-films. First, a previously developed kinetic Monte-Carlo (kMC) model is adapted for the multiscale simulation to describe the surface reactions. Then, two macroscopic models, specifically tailored for the remote plasma reactor, are formulated to describe the dynamic behaviors of the plasma generation and bulk species transport domains, respectively. Additionally, an integrated message passing interface (MPI) scheme is built to couple and resolve the communication between different scales in the model. The model results are then validated with experimental data and an automated workflow is established for model calculations without human intervention. Finally, a set of baseline operating conditions derived from the model simulation results is proposed to guarantee the production of high-quality thin-films. [ABSTRACT FROM AUTHOR]

Highlights: [•] Novel carbazole derivatives containing quinoline were synthesized. [•] Electronic transition of carbazole derivatives were theoretically studied by TD-DFT. [•] Long fluorescence lifetime of carbazole derivatives was obtained. [•] Two-photon absorption of compounds was measured by 120fs pulse at 800nm. [ABSTRACT FROM AUTHOR]