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The deep western boundary current (DWBC) in the Philippine Sea has been expected to play a crucial role in transporting lower circumpolar deep water and to contribute to regional and global climate regulation. Two-year-long near-bottom current measurements reveal a southward-flowing DWBC with a mean velocity of 5 cm/s and seasonal variations—weaker in summer and stronger in winter. Seasonal variability in the DWBC is hypothesized to be induced by changes in the North Equatorial Current bifurcation latitude (NECBL) and upper pycnocline depth through potential vorticity conservation. Data-assimilated reanalysis model (GLORYS12V1) outputs, which reproduce the seasonal variability of DWBC similarly to the observation, are used for further analysis. During the seasonal period, the NECBL displays significant coherence (>0.9) with the first-mode empirical orthogonal function principal component of the simulated along-slope DWBC. The upper pycnocline depth, varying seasonally within a range of approximately 27 m, induces seasonal variability in a deep anticyclonic eddy trapped by topography. In summer, the intensified deep anticyclonic eddy obstructs the adjacent southward-flowing DWBC, weakening its strength, whereas in winter, the southward flow of the DWBC is enhanced due to the weakening of the deep anticyclonic eddy. [ABSTRACT FROM AUTHOR]

Thermomechanical analysis of monolithic microwave integrated circuit (MMIC) packaging is essential to guarantee the reliability of radio frequency/microwave applications. However, a method for fast and accurate analysis of MMIC packaging structures has not been developed. Here, a machine learning (ML)‐based solution for thermomechanical analysis of MMIC packaging is demonstrated. This ML‐based solution analyzes temperature and thermal stresses considering key design parameters, including material properties, geometric characteristics, and thermal boundary conditions. Finite element simulation with the Monte Carlo method is utilized to prepare a large dataset for supervised learning and validation of the ML solution, and a laser‐assisted thermal experiment is conducted to verify the accuracy of the simulation. After data preparation, regression tree ensemble and artificial neural network (ANN) learning models are investigated. The results show that the ANN model accurately predicts the outcomes with extremely low computing time by analyzing the high‐dimensional dataset. Finally, the developed ML solution is deployed as a web application format for facile approaches. It is believed that this study will provide a guideline for developing ML‐based solutions in chip packaging design technology. [ABSTRACT FROM AUTHOR]

Li+ electrolyte-gated transistors (EGTs) have attracted significant attention as artificial synapses because of the fast response of Li+ ion, low operating voltage, and applicability to flexible electronics. Due to the inherent nature of Li+ ion, Li+ EGTs show, however, limitations, such as poor long-term synaptic plasticity and nonlinear/nonsymmetric conductance update, which hinder the practical applications of artificial synapses. Herein, Li+ EGTs integrated with poly(vinylidene fluoride-co-trifluoroethylene) (PVDF-TrFE) ferroelectric polymer as a channel–electrolyte interlayer are presented. Owing to the polarized domains of PVDF-TrFE, the transport of Li+ ions at the channel–electrolyte interface is accelerated, and Li+ ions effectively penetrate the channel. Moreover, the self-diffusion of Li+ ions from the channel to the electrolyte is suppressed by the downward polarized domains. Li+ EGTs, therefore, successfully demonstrate synaptic characteristics, including excitatory postsynaptic current, short-/long-term synaptic plasticity, and paired-pulse facilitation. Also, conductance update in Li+ EGTs shows a dynamic range (Gmax/Gmin) of 92.42, high linearity, and distinct stability over 100 cycles. Based on their synaptic characteristics, inference simulations using a convolution neural network for the CIFAR-10 dataset imply that Li+ EGTs are suitable as artificial synapses with an inference accuracy of 89.13%. The new methodological approach addressing modulation of ion dynamics at the interface is introduced for developing practical synaptic devices. [ABSTRACT FROM AUTHOR]

We present a fully analytical model and physical investigation on the source resistance (RS) in InxGa1−xAs quantum-well high-electron mobility transistors based on a three-layer TLM system. The RS model in this work was derived by solving the coupled quadratic differential equations for each current component with appropriate boundary conditions, requiring only six physical and geometrical parameters, including ohmic contact resistivity (ρc), barrier tunneling resistivity (ρbarrier), sheet resistances of the cap and channel regions (Rsh_cap and Rsh_ch), side-recessed length (Lside) and gate-to-source length (Lgs). To extract each model parameter, we fabricated two different TLM structures, such as cap-TLM and recessed-TLM. The developed RS model in this work was in excellent agreement with the RS values measured from the two TLM devices and previously reported short-Lg HEMT devices. The findings in this work revealed that barrier tunneling resistivity already played a critical role in reducing the value of RS in state-of-the-art HEMTs. Unless the barrier tunneling resistivity is reduced considerably, innovative engineering on the ohmic contact characteristics and gate-to-source spacing would only marginally improve the device performance. [ABSTRACT FROM AUTHOR]

In this study, we investigated the impact of intrinsic output conductance (goi) on the short-circuit current-gain cut-off frequency (fT) in InxGa1-xAs/In0.52Al0.48As quantum-well (QW) high-electron-mobility transistors. At its core, we attempted to extract values of fT using a simplified small-signal model (SSM) of the HEMTs and to derive an analytical formula for fT in terms of extrinsic model parameters that are related with intrinsic model parameters of a general SSM. We projected how fT was influenced by goi in HEMTs, emphasizing that the improvement in electrostatic integrity would also be of critical importance to fully benefit from scaling down Lg. [ABSTRACT FROM AUTHOR]

The roll-to-roll (R2R) continuous patterning of silver nanowire-polyvinylpyrrolidone (Ag NW-PVP) composite transparent conductive film (cTCF) is demonstrated in this work by means of slot-die coating followed by selective calendering. The Ag NWs were synthesized by the polyol method, and adequately washed to leave an appropriate amount of PVP to act as a capping agent and dispersant. The as-coated Ag NW-PVP composite film had low electronic conductivity due to the lack of percolation path, which was greatly improved by the calendering process. Moreover, the dispersion of Ag NWs was analyzed with addition of PVP in terms of density and molecular weight. The excellent dispersion led to uniform distribution of Ag NWs in a cTCF. The continuous patterning was conducted using an embossed pattern roll to perform selective calendering. To evaluate the capability of the calendering process, various line widths and spacing patterns were investigated. The minimum pattern dimensions achievable were determined to be a line width of 0.1 mm and a line spacing of 1 mm. Finally, continuous patterning using selective calendering was applied to the fabrication of a flexible heater and a resistive touch sensing panel as flexible electronic devices to demonstrate its versatility. [ABSTRACT FROM AUTHOR]