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Real-Space and Real-Time Propagation for Correlated Electron-Nuclear Dynamics Based on Exact Factorization
No full textWe present coupled equations of motion for correlated electron-nuclear dynamics for real-space and real-time propagation with a proper electron-nuclear correlation (ENC) from the exact factorization. Since the original ENC term from the exact factorization is non-Hermitian, the numerical instability arises as we propagate an electronic wave function. In this paper, we propose a Hermitian-type ENC term which depends on the electron density matrix and the nuclear quantum momentum. Moreover, we show that the Hermitian property of the electron- nuclear correlation term can capture quantum (de)coherence with a stable numerical real-space and real-time propagation. As an application, we demonstrate a real-space and real-time propagation of an electronic wave function coupled to trajectory-based nuclear motion for a one-dimensional model Hamiltonian. Our approach can capture nonadiabatic phenomena as well as quantum decoherence in excited state molecular dynamics. In addition, we propose a scheme to extend the current approach to many-body electronic states based on real-time time-dependent density functional theory, testing the nonadiabatic dynamics of a simple molecular system
Non-volatile charge-trap memory characteristics with low-temperature atomic layer deposited HfO2-x charge-trap layer and interfacial tunneling oxide formed by UV/ozone treatment
No full textNon-volatile charge-trap memory characteristics were investigated in the thin-film transistors with an indium-gallium-zinc oxide channel and an oxygen-deficient hafnium oxide (HfO2-x) charge-trap layer de-posited by atomic layer deposition (ALD). The HfO2-x charge-trap layer was subsequently UV/ozone treated to convert its surface to more insulating tunneling oxide. The HfO2-x charge-trap layer deposited at a low temperature of 50 degrees C was found to have a high defect density to store electrons with an areal density of 1.57 x 1012 cm-2 calculated from the threshold voltage (VT) shift of 30 V. The VT shifted positively as a result of electron charging in the charge-trap layer by applying positive gate bias, and then reduced back re-versibly by applying negative bias. Also, the shifted VT is retained over time, rendering non-volatile memory characteristics. The improved reliability of charge storage is expected to come from eliminating trap states at the interface by UV/ozone treatment. It also simplifies the overall fabrication process by removing ad-ditional processing steps of tunneling oxide layer deposition. The proposed device turned out to have a large memory window thanks to the high trap density in the HfO2-x charge-trap layer deposited by low -tem-perature ALD and the good non-volatility by using UV/ozone treatment.(c) 2023 Elsevier B.V. All rights reserved
Estimation of Inactivation time for the SARS-CoV-2 virus from the UV biometer in South Korea
No full textThe coronavirus disease 2019 (COVID-19) is a result of the infection by "severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and has caused various social and economic effects over the globe. As the SARS-CoV-2 is effectively inactivated by the exposure to the UV-B radiation (shorter than 315 nm), the exposure time for inactivation of the SARS-CoV-2 was estimated using the broadband UV observation instrument over 11 observation sites in South Korea. For the limitation of the UV biometer, which has limited spectral information, the coefficient for conversion from the erythemal UV (EUV) to the radiation for virus inactivation was adopted before estimating the inactivation time. The inactivation time of SARS-CoV-2 is significantly dependent on seasonal and diurnal variations due to the temporal variations of surface incident UV irradiance. The inactivation times in summer and winter were around 10 and 50 min, respectively. The inactivation time was unidentified during winter afternoons due to the weak spectral UV solar radiation in winter. As the estimation of inactivation time using broadband observation includes the uncertainty due to the conversion coefficient and the error due to the solar irradiance, the sensitivity analysis of the inactivation time estimation was also conducted by changing the UV irradiance
Heterosynaptic MoS2 Memtransistors Emulating Biological Neuromodulation for Energy-Efficient Neuromorphic Electronics
No full textHeterosynaptic neuromodulation is a key enabler for energy-efficient and high-level biological neural processing. However, such manifold synaptic modulation cannot be emulated using conventional memristors and synaptic transistors. Thus, reported herein is a three-terminal heterosynaptic memtransistor using an intentional-defect-generated molybdenum disulfide channel. Particularly, the defect-mediated space-charge-limited conduction in the ultrathin channel results in memristive switching characteristics between the source and drain terminals, which are further modulated using a gate terminal according to the gate-tuned filling of trap states. The device acts as an artificial synapse controlled by sub-femtojoule impulses from both the source and gate terminals, consuming lower energy than its biological counterpart. In particular, electrostatic gate modulation, corresponding to biological neuromodulation, additionally regulates the dynamic range and tuning rate of the synaptic weight, independent of the programming (source) impulses. Notably, this heterosynaptic modulation not only improves the learning accuracy and efficiency but also reduces energy consumption in the pattern recognition. Thus, the study presents a new route leading toward the realization of highly networked and energy-efficient neuromorphic electronics
MicroDeblur: Image Motion Deblurring on Microcontroller-based Vision Systems
No full textThis paper introduces MicroDeblur, an on-device image motion deblur solution for resource-constrained microcontroller-based vision systems. Although motion blurs caused by the movement or shake of the device (camera) are pervasive in embedded, IoT, and mobile devices, it has been considered a hard nut to crack for many microcontrollers with extremely-limited resources (e.g., hundreds of KB of RAM). To tackle this problem, we combine the DNN (deep neural network) motion deblur method with the classical motion deblur approach and take the best of both worlds, i.e., 1) powerful pattern recognition ability of DNNs and 2) simplicity and stability of matrix-based classical algorithms. To deblur an image, MicroDeblur takes three steps: 1) blur kernel estimation, 2) blur image transformation, and 3) iterative clear image restoration. We propose 1) depth-independent convolution that efficiently estimates the blur kernel (pattern) and 2) Toeplitz-based motion blur modeling that enhances the time and space complexity of the deblurring process by and , respectively, compared to the existing methods. To the best of our knowledge, MicroDeblur is the first self-sufficient blind deconvolution solution for a stand-alone microcontroller that does not rely on extra hardware or external systems. We implement MicroDeblur on an ARM Cortex-M4F, achieving a competitive quality of deblurred images using 187x and 429x smaller memory and energy, respectively, compared to high-end GPU-based solutions
Acid- and Gas-Scavenging Electrolyte Additive Improving the Electrochemical Reversibility of Ni-Rich Cathodes in Li-Ion Batteries
No full textIn view of their high theoretical capacities, nickel-rich layered oxides are promising cathode materials for high-energy Li-ion batteries. However, the practical applications of these oxides are hindered by transition metal dissolution, microcracking, and gas/reactive compound formation due to the undesired reactions of residual lithium species. Herein, we show that the interfacial degradation of the LiNi0.9CoxMnyAlzO2 (NCMA, x + y + z = 0.1) cathode and the graphite (Gr) anode of a representative Li-ion battery by HF can be hindered by supplementing the electrolyte with tert-butyldimethylsilyl glycidyl ether (tBS-GE). The silyl ether moiety of tBS-GE scavenges HF and PF5, thus stabilizing the interfacial layers on both electrodes, while the epoxide moiety reacts with CO2 released by the parasitic reaction between HF and Li2CO3 on the NCMA surface to afford cyclic carbonates and thus suppresses battery swelling. NCMA/Gr full cells fabricated by supplementing the baseline electrolyte with 0.1 wt % tBS-GE feature an increased capacity retention of 85.5% and deliver a high discharge capacity of 162.9 mAh/g after 500 cycles at 1 C and 25 degrees C. Thus, our results reveal that the molecular aspect-based design of electrolyte additives can be efficiently used to eliminate reactive species and gas components from Li-ion batteries and increase their performance
Stabilization of photoactive phases for perovskite photovoltaics
No full textInterest in photovoltaics (PVs) based on Earth-abundant halide perovskites has increased markedly in recent years owing to the remarkable properties of these materials and their suitability for energy-efficient and scalable solution processing. Formamidinium lead triiodide (FAPbI(3))-rich perovskite absorbers have emerged as the frontrunners for commercialization, but commercial success is reliant on the stability meeting the highest industrial standards and the photoactive FAPbI(3) phase suffers from instabilities that lead to degradation - an effect that is accelerated under working conditions. Here, we critically assess the current understanding of these phase instabilities and summarize the approaches for stabilizing the desired phases, covering aspects from fundamental research to device engineering. We subsequently analyse the remaining challenges for state-of-the-art perovskite PVs and demonstrate the opportunities to enhance phase stability with ongoing materials discovery and in operando analysis. Finally, we propose future directions towards upscaling perovskite modules, multijunction PVs and other potential applications. [GRAPHICS]
CIE Colorimetric Mismatch Between Two Different Liquid Crystal Displays - Focused on Metamerism
No full textRecently, various studies have observed the phenomenon that the two colors having the same CIE 1931 XYZ tristimulus values on two different displays are perceived as different colors. This study conducted a color matching experiment at a 4?? viewing angle using two LCD displays with different spectral characteristics followed. Participants performed three color matching experiments: two adjustment methods and one color selection method. The results showed that although there was an observer variability of 2.78??1.96 ???E00, it was confirmed that the CIE colorimetric mismatch magnitude was approximately twice as large as the observer variability magnitude; 5.49??2.27 ???E00. Based on the color matching experiment results, the color appearance experiment which evaluates hue and color difference was conducted. The results showed that the results of the color matching experiment and the color appearance experiment were consistent. For the colors matched by each individual, the participants responded that colors were neutral colors which felt a little or no hue, and the color difference with the reference stimulus was low. This study confirmed that CIE 1931 color matching function failed to explain the color matching results, and research on new color matching functions considering human physiological characteristics is needed
Development of a Deep Learning-based Uncertainty-aware Predictive Maintenance Method
No full textResearch on predictive maintenance strategies for industrial electronics has improved from corrective and preventive strategies, which require a large amount of time and cost, to data-driven intelligent systems. In particular, data-driven methods monitor the real-time condition and health status of industrial electronics to facilitate efficient maintenance. Based on the real data collected from electronics, machine learning methods have been widely employed to analyze the health status of industrial electronics in advance of the unpredicted shutdown. Recently, the use of deep learning-based methods that have higher expressive power and adaptability is increasing in order to yield more accurate and stable prognostic results. However, there still exist some problems in the current form of predictive maintenance strategies. First, prognostic results are mostly point estimates, which lack not only predictive uncertainties but also interpretabilities for domain experts on site. Furthermore, some existing uncertainty-aware prediction methods are not scalable to large-scale multivariate data collected from industrial electronics. In this research, a Bayesian learning-based prognostic method is proposed for predictive maintenance of industrial electronics. The proposed method provides uncertainty-aware prediction results that are scalable to large-scale datasets. The effectiveness of the proposed prognostic method is validated using multiple datasets as well as is compared with existing deterministic and probabilistic methods