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Dinaphtho[2,3-b:2',3'-f]thieno[3,2-b]thiophene (DNTT) is a widely used small-molecular p-type organic semiconductor. Despite the broad availability of high-performance DNTT transistors, there is a lack of investigation into other devices based on this semiconductor. In this study, rectifying diodes with DNTT as a single transport medium are fabricated and characterized. Realizing unipolar current rectification from asymmetric metal contacts, a number of physical and electrical properties of DNTT are made accessible. Current–voltage measurement, broad-band impedance spectroscopy, drift-diffusion simulation, and equivalent-circuit modeling are combined to quantify important parameters such as dielectric constant, trap energy, and lifetime. These results provide a practical reference for the design and optimization of diverse electronic devices incorporating DNTT. [ABSTRACT FROM AUTHOR]

We investigate a synaptic device with short-term memory characteristics using IGZO/SnOx as the switching layer. The thickness and components of each layer are analyzed by using x-ray photoelectron spectroscopy and transmission electron microscopy. The memristor exhibits analog resistive switching and a volatile feature with current decay over time. Moreover, through ten cycles of potentiation and depression, we demonstrate stable conductance modulation, leading to high-accuracy Modified National Institute of Standards and Technology pattern recognition. We effectively emulate the learning system of a biological synapse, including paired-pulse facilitation, spiking-amplitude-dependent plasticity, and spiking-rate-dependent plasticity (SRDP) by pulse trains. Ultimately, 4-bit reservoir computing divided into 16 states is incarnated using a pulse stream considering short-term memory plasticity and decay properties. [ABSTRACT FROM AUTHOR]

We present an approach to adopt deep neural networks for the development of a compact model for transistors, namely a neural compact model, including transfer learning to enhance accuracy and reduce model development time. We examine the effectiveness of this approach when the electrical data for neural networks is scarce and costly and when the electrical characteristics to be modeled are highly non-linear. By using technology computer-aided design simulations, we constructed a dataset of the electrical characteristics of organic field-effect transistors with Gaussian disorder that exhibit highly non-linear current–voltage curves. Subsequently, we developed neural compact models by modifying conventional deep learning models and validated the effectiveness of transfer learning with testing through various experiments. We showed that the neural compact model with transfer learning provides an equivalent accuracy at a significantly shorter training time. [ABSTRACT FROM AUTHOR]

In this study, the channel potential of inhibited strings in 3D NAND flash memory using a bandgap-engineered tunneling oxide (BE-TOX) structure is analyzed. The equivalent oxide thickness (EOT) of the structure using BE-TOX was designed to be the same as the conventional 3D NAND flash memory, and the channel potentials of the down coupling phenomenon (DCP) and natural local self-boosting (NLSB) effect were analyzed. As a result, the BE-TOX structure was confirmed to have a higher channel potential in the DCP and NLSB than the conventional structure, making it relatively effective for program disturbance. The main reason for the difference in the channel potential between the BE-TOX and conventional structures is that adjacent cells have different threshold voltages (Vth). When the same program voltage (VPGM) and program time (TPGM) were applied during the program operation, Vth decreased in the BE-TOX structure, which increased the channel potential when DCP and NLSB occurred. Finally, a simulation was conducted by varying the thicknesses of the oxide and nitride in the BE-TOX structure. Despite the EOT being fixed and the thicknesses of both nitride and oxide being varied, the channel potential was affected. [ABSTRACT FROM AUTHOR]

We present a drain current model for coplanar organic field-effect transistors (OFETs) considering temperature dependent power-law mobility and contact resistance. By solving a microscopic two-dimensional equivalent circuit model, we demonstrate that the power-law dependence of mobility on the gate voltage leads to the asymptotic power-law dependence of the contact resistance. Furthermore, we validated the model by measuring the transfer characteristics of pentacene-based coplanar OFETs in a wide temperature range between 293 K and 373 K. We study the temperature dependence of the characteristic temperature on the power-law exponent and the on-state resistance. We showed that the characteristic temperature is independent of the temperature and the on-state resistance follows an inverse Arrhenius-type law. [ABSTRACT FROM AUTHOR]

We present technology computer aided design (TCAD) results for wide band-gap Sn-doped α-Ga2O3 metal–semiconductor field-effect transistors (MESFETs). In particular, the effect of gate work function and electrode gap length on the electrical characteristics is demonstrated for a thorough understanding of the behavior of such devices. The gate work function significantly affects the reverse bias drain current under the gate-current dominant regime, whereas a gate-source/drain gap larger than 0.1 µm has a negligible effect on the drain current. [ABSTRACT FROM AUTHOR]