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This paper proposes an effective method to improve the power efficiency of the flyback converter in the continuous conduction mode (CCM). The proposed converter uses a simple passive circuit to reduce the switching power losses. The current through the output diode can be shifted to a new branch where one diode, one inductor, and one auxiliary winding of the transformer are included. The output diode current can be reduced to zero for the zero-current switching of the output diode. The additional inductor is used to control the changing rate of the additional diode current to reduce the reverse-recovery current. Keeping the simplicity of the passive method, the proposed converter improves the power efficiency compared to the conventional converter. The circuit configuration and the operation principle are described. The design considerations are presented, including the simulation verification. The experimental results for a 45 W prototype are discussed to evaluate the performance of the proposed converter. The proposed converter has achieved a power efficiency of 93.5% for the rated load condition, improving the power efficiency. The applications of the proposed converter are also discussed for the future research directions. [ABSTRACT FROM AUTHOR]

Smart healthcare systems integrated with advanced deep neural networks enable real‐time health monitoring, early disease detection, and personalized treatment. In this work, a novel 3D AND‐type flash memory array with a rounded double channel for computing‐in‐memory (CIM) architecture to overcome the limitations of conventional smart healthcare systems: the necessity of high area and energy efficiency while maintaining high classification accuracy is proposed. The fabricated array, characterized by low‐power operations and high scalability with double independent channels per floor, exhibits enhanced cell density and energy efficiency while effectively emulating the features of biological synapses. The CIM architecture leveraging the fabricated array achieves high classification accuracy (93.5%) for electrocardiogram signals, ensuring timely detection of potentially life‐threatening arrhythmias. Incorporated with a simplified spike‐timing‐dependent plasticity learning rule, the CIM architecture is suitable for robust, area‐ and energy‐efficient in‐memory arrhythmia detection systems. This work effectively addresses the challenges of conventional smart healthcare systems, paving the way for a more refined healthcare paradigm. [ABSTRACT FROM AUTHOR]

A high‐responsivity photo‐field‐effect transistor (photo‐FET) with a metal‐oxide‐semiconductor (MOS) structure is a promising technology for low‐intensity light detection with its high gain and low operation voltage. To enhance their responsivity, the equivalent oxide thickness (EOT) scaling is one of the effective solutions, which is a common technology to improve the electrical properties of MOSFETs using higher‐k insulators. Herein, the EOT scaling effect on the optoelectrical characteristics of photo‐FETs using Al2O3 and Al2O3/HfO2 gate stacks is investigated. Thanks to the EOT scaling effect introducing Al2O3/HfO2, only the transconductance of the photo‐FET is enhanced without any significant change in the photovoltaic effect and cavity effect. As a result, its responsivity is improved by up to 1.7 times. The results give a basic strategy of the EOT scaling effect for photo‐FETs; thus, the EOT scaling with a higher‐k insulator is a powerful solution for the high‐performance InGaAs photo‐FET requiring high responsivity in the short‐wavelength infrared range. [ABSTRACT FROM AUTHOR]

There is a need to design a hardware synapse array appropriate for enhancing the efficiency of neuromorphic computing systems while minimizing energy consumption. This study introduces a memristor device with an AlOx overshoot suppression layer (A‐OSL) to achieve a self‐compliance effect. By optimizing each cell within the 16 × 16 crossbar array, synaptic devices are successfully fabricated with reliable characteristics and 3‐bit multilevel capabilities. In addition, the oxygen composition of TiOx and the annealing conditions are optimized to reduce the forming voltage and minimize the variation in the switching voltage. As a result, stable forming‐free characteristics are obtained through A‐OSL insertion, a reduction in forming voltage, and TiOx oxygen composition optimization. Also, target weights are accurately transferred to the A‐OSL memristor crossbar array and conducted the inference process by applying spike signals to the array following the designated time step. The spiking neural network (SNN) is demonstrated by measuring vector‐matrix multiplication (VMM) of the 16 × 16 crossbar array. The VMM results exhibit a classification accuracy of 90.80% for the MNIST dataset, which is close to the accuracy achieved by software‐based approaches, amounting to 91.85%. [ABSTRACT FROM AUTHOR]

This study implements a highly uniform 3D vertically stack resistive random‐access memory (VRRAM) with a four‐layer contact hole structure. The fabrication process of a four‐layer VRRAM is demonstrated, and its physical and electrical properties are thoroughly examined. X‐ray photoelectron spectroscopy and transmission electron microscopy are employed to analyze the chemical distribution and physical structure of the VRRAM device. Multilevel capability, reliable endurance (>104 cycles), and retention (104 s) are successfully obtained. Synaptic memory plasticity, such as spike time‐dependent plasticity, spike rate‐dependent plasticity, excitatory post‐synaptic current, paired‐pulse facilitation, and long‐term potentiation and depression is presented. Finally, the vector‐matrix multiplication (VMM) operation is conducted on a 4 × 12 VRRAM array, according to the low resistance state ratio. It is ascertained that the accuracy drop, which can occur due to VMM error, can be limited to a decrease of less than 0.44% point. Utilizing the high‐density, multilevel, and biological characteristics of VRRAM, it is possible to implement high‐performance neuromorphic systems that require densely integrated synaptic devices. [ABSTRACT FROM AUTHOR]