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Metal oxide thin films are critical components in modern electronic applications. In particular, high-κ dielectrics are of interest for reducing power consumption in metal-insulator-semiconductor (MIS) field-effect transistors. Although thin-film materials are typically produced via vacuum-based methods, solution deposition offers a scalable and cost-efficient alternative. We report an all-inorganic aqueous solution route to amorphous lanthanum zirconium oxide (LaZrO, LZO) dielectric thin films. LZO films were spin-cast from aqueous solutions of metal nitrates and annealed at temperatures between 300 and 600 °C to produce dense, defect-free, and smooth films with subnanometer roughness. Dielectric constants of 12.2-16.4 and loss tangents <0.6% were obtained for MIS devices utilizing LZO as the dielectric layer (1 kHz). Leakage currents <10 A cm at 4 MV cm were measured for samples annealed at 600 °C. The excellent surface morphology, high dielectric constants, and low leakage current densities makes these LZO dielectrics promising candidates for thin-film transistor devices.

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This paper presents a new method for studying protein folding kinetics. It uses the recently introduced Stochastic Roadmap Simulation (SRS) method to estimate the transition state ensemble (TSE) and predict the rates and the Phi-values for protein folding. The new method was tested on 16 proteins, whose rates and Phi-values have been determined experimentally. Comparison with experimental data shows that our method estimates the TSE much more accurately than an existing method based on dynamic programming. This improvement leads to better folding-rate predictions. We also compute the mean first passage time of the unfolded states and show that the computed values correlate with experimentally determined folding rates. The results on Phi-value predictions are mixed, possibly due to the simple energy model used in the tests. This is the first time that results obtained from SRS have been compared against a substantial amount of experimental data. The results further validate the SRS method and indicate its potential as a general tool for studying protein folding kinetics.

The impact of decreasing channel layer thickness on the electrical performance of RF-sputtered amorphous indiumgallium-zinc oxide (a-IGZO) thin-film transistors (TFTs) is investigated through the evaluation of drain current versus gate voltage (I D -V G ) transfer curves. For a fixed set of process parameters, it is found that the turn-on voltage, V ON (off-drain current, I OFF D ) increases (decreases) with decreasing a-IGZO channel layer thickness (h) for h < 11 nm. The V ON − h trend is attributed to a large density (3.5 × 10 12 cm −2 ) of backside surface acceptorlike traps and an enhanced density (3 × 10 18 cm −3 ) of donorlike trap states within the upper 11 nm from the backside surface. The precipitous decrease observed in I OFF D − h when h < 11 nm is ascribed to backside surface acceptorlike traps and the closer physical proximity of the backside surface when the channel layer is ultrathin. An alteration of the sputtering process gas ratio of Ar/O 2 from 9/1 to 10/0 and a reduction of the annealing temperature from 400°C to 150°C result in improved transistor performance for a h ≈ 5 nm a-IGZO TFT, characterized by V ON ≈ 0 V, field-effect mobility of µ FE = 9 cm 2 V −1 s −1 , subthreshold swing of S = 90 mV/decade, and drain current on-to-off ratio of I ON-OFF D = 2 × 10 5 .Index Terms-amorphous materials, thin films, thin film transistors, semiconductor-insulator interfaces, charge carrier density.

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Molecular dynamics (MD) simulation is a well-established method for studying protein motion at the atomic scale. However, it is computationally intensive and generates massive amounts of data. One way of addressing the dual challenges of computation efficiency and data analysis is to construct simplified models of long-timescale protein motion from MD simulation data. In this direction, we propose to use Markov models with hidden states, in which the Markovian states represent potentially overlapping probabilistic distributions over protein conformations. We also propose a principled criterion for evaluating the quality of a model by its ability to predict long-timescale protein motions. Our method was tested on 2D synthetic energy landscapes and two extensively studied peptides, alanine dipeptide and the villin headpiece subdomain (HP-35 NleNle). One interesting finding is that although a widely accepted model of alanine dipeptide contains six states, a simpler model with only three states is equally good for predicting long-timescale motions. We also used the constructed Markov models to estimate important kinetic and dynamic quantities for protein folding, in particular, mean first-passage time. The results are consistent with available experimental measurements.Contact: chiangts@comp.nus.edu.sg