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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]

Hardware neuromorphic systems are crucial for the energy‐efficient processing of massive amounts of data. Among various candidates, hafnium oxide ferroelectric tunnel junctions (FTJs) are highly promising for artificial synaptic devices. However, FTJs exhibit non‐ideal characteristics that introduce variations in synaptic weights, presenting a considerable challenge in achieving high‐performance neuromorphic systems. The primary objective of this study is to analyze the origin and impact of these variations in neuromorphic systems. The analysis reveals that the major bottleneck in achieving a high‐performance neuromorphic system is the dynamic variation, primarily caused by the intrinsic 1/f noise of the device. As the device area is reduced and the read bias (VRead) is lowered, the intrinsic noise of the FTJs increases, presenting an inherent limitation for implementing area‐ and power‐efficient neuromorphic systems. To overcome this limitation, an adaptive read‐biasing (ARB) scheme is proposed that applies a different VRead to each layer of the neuromorphic system. By exploiting the different noise sensitivities of each layer, the ARB method demonstrates significant power savings of 61.3% and a scaling effect of 91.9% compared with conventional biasing methods. These findings contribute significantly to the development of more accurate, efficient, and scalable neuromorphic systems. [ABSTRACT FROM AUTHOR]

Herein, dual‐gate field‐effect transistors (DG FETs) fabricated on Si substrate and a corresponding NOR‐type array designed for low‐power on‐chip trainable hardware neural networks (HNNs) are presented. The fabricated DG FET exhibits notable endurance characteristics, with the subthreshold swing remaining consistently within a 2.45% range of change and ΔVth per cycle maintaining stability at 4.5% over repetitive program and erase operations, up to 104 cycles. Furthermore, a multilevel characteristic is achieved through low‐power program/erase operations based on Fowler–Nordheim (FN) tunneling, which exhibit 0.09 and 0.99 fJ per spike, respectively. These characteristics provide the HNN stability, along with high performance and power efficiency. The NOR‐type array in this work demonstrates selective update and bidirectional vector‐by‐matrix multiplication capabilities. This enables on‐chip training based on a gradient descent algorithm, without the need for an additional array for backpropagation. Subsequently, a simulation of the Modified National Institute of Standards and Technology classification is conducted to evaluate the accuracy and training power consumption of the proposed device in comparison to other two‐terminal memristor devices. The results show that the DG FET array achieves superior accuracy while maintaining over 180.4 times higher energy efficiency, demonstrating the potential of the DG FET as a promising candidate for low‐power HNN applications. [ABSTRACT FROM AUTHOR]

We present high performance gas sensors based on an array of near single crystalline TiO2 nanohelices fabricated by rotating oblique angle deposition (OAD). The combination of large surface-to-volume ratio, extremely small size (＜30 nm) comparable to the Debye length, a near single crystallinity of TiO2 nanohelices, together with the unique top-and-bottom electrode configuration hugely improves the H2-sensing performance, including ∼10 times higher response at 50 ppm, approximately a factor of 5 lower detection limit, and much faster response time than the conventional TiO2 thin film devices. Beyond such remarkable performance enhancement, the excellent compatibility of the OAD method compared with the conventional micro-fabrication technology opens a new avenue for monolithic integration of high-performance chemoresistive sensors to fabricate a simple, low cost, reliable, yet fully functional electronic nose and multi-functional smart chips for in situ environmental monitoring. [ABSTRACT FROM AUTHOR]

We present high performance gas sensors based on an array of near single crystalline TiO(2) nanohelices fabricated by rotating oblique angle deposition (OAD). The combination of large surface-to-volume ratio, extremely small size (<30 nm) comparable to the Debye length, a near single crystallinity of TiO(2) nanohelices, together with the unique top-and-bottom electrode configuration hugely improves the H(2)-sensing performance, including ∼10 times higher response at 50 ppm, approximately a factor of 5 lower detection limit, and much faster response time than the conventional TiO(2) thin film devices. Beyond such remarkable performance enhancement, the excellent compatibility of the OAD method compared with the conventional micro-fabrication technology opens a new avenue for monolithic integration of high-performance chemoresistive sensors to fabricate a simple, low cost, reliable, yet fully functional electronic nose and multi-functional smart chips for in situ environmental monitoring.