Demonstration of In-Memory Biosignal Analysis: Novel High-Density and Low-Power 3D Flash Memory Array for Arrhythmia Detection.

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.

Proposition of Adaptive Read Bias: A Solution to Overcome Power and Scaling Limitations in Ferroelectric-Based Neuromorphic System.

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.

Efficient Hybrid Training Method for Neuromorphic Hardware Using Analog Nonvolatile Memory.

Neuromorphic hardware using nonvolatile analog synaptic devices provides promising advantages of reducing energy and time consumption for performing large-scale vector-matrix multiplication (VMM) operations. However, the reported training methods for neuromorphic hardware have appreciably shown reduced accuracy due to the nonideal nature of analog devices, and use conductance tuning protocols that require substantial cost for training. Here, we propose a novel hybrid training method that efficiently trains the neuromorphic hardware using nonvolatile analog memory cells, and experimentally demonstrate the high performance of the method using the fabricated hardware. Our training method does not rely on the conductance tuning protocol to reflect weight updates to analog synaptic devices, which significantly reduces online training costs. When the proposed method is applied, the accuracy of the hardware-based neural network approaches to that of the software-based neural network after only one-epoch training, even if the fabricated synaptic array is trained for only the first synaptic layer. Also, the proposed hybrid training method can be efficiently applied to low-power neuromorphic hardware, including various types of synaptic devices whose weight update characteristics are extremely nonlinear. This successful demonstration of the proposed method in the fabricated hardware shows that neuromorphic hardware using nonvolatile analog memory cells becomes a more promising platform for future artificial intelligence.

Energy Efficient Artificial Olfactory System with Integrated Sensing and Computing Capabilities for Food Spoilage Detection.

Artificial olfactory systems (AOSs) that mimic biological olfactory systems are of great interest. However, most existing AOSs suffer from high energy consumption levels and latency issues due to data conversion and transmission. In this work, an energy- and area-efficient AOS based on near-sensor computing is proposed. The AOS efficiently integrates an array of sensing units (merged field effect transistor (FET)-type gas sensors and amplifier circuits) and an AND-type nonvolatile memory (NVM) array. The signals of the sensing units are directly connected to the NVM array and are computed in memory, and the meaningful linear combinations of signals are output as bit line currents. The AOS is designed to detect food spoilage by employing thin zinc oxide films as gas-sensing materials, and it exhibits low detection limits for H2 S and NH3 gases (0.01 ppm), which are high-protein food spoilage markers. As a proof of concept, monitoring the entire spoilage process of chicken tenderloin is demonstrated. The system can continuously track freshness scores and food conditions throughout the spoilage process. The proposed AOS platform is applicable to various applications due to its ability to change the sensing temperature and programmable NVM cells.

Reconfigurable neuromorphic computing block through integration of flash synapse arrays and super-steep neurons.

Neuromorphic computing (NC) architecture inspired by biological nervous systems has been actively studied to overcome the limitations of conventional von Neumann architectures. In this work, we propose a reconfigurable NC block using a flash-type synapse array, emerging positive feedback (PF) neuron devices, and CMOS peripheral circuits, and integrate them on the same substrate to experimentally demonstrate the operations of the proposed NC block. Conductance modulation in the flash memory enables the NC block to be easily calibrated for output signals. In addition, the proposed NC block uses a reduced number of devices for analog-to-digital conversions due to the super-steep switching characteristics of the PF neuron device, substantially reducing the area overhead of NC block. Our NC block shows high energy efficiency (37.9 TOPS/W) with high accuracy for CIFAR-10 image classification (91.80%), outperforming prior works. This work shows the high engineering potential of integrating synapses and neurons in terms of system efficiency and high performance.

Self-Curable Synaptic Ferroelectric FET Arrays for Neuromorphic Convolutional Neural Network.

With the recently increasing prevalence of deep learning, both academia and industry exhibit substantial interest in neuromorphic computing, which mimics the functional and structural features of the human brain. To realize neuromorphic computing, an energy-efficient and reliable artificial synapse must be developed. In this study, the synaptic ferroelectric field-effect-transistor (FeFET) array is fabricated as a component of a neuromorphic convolutional neural network. Beyond the single transistor level, the long-term potentiation and depression of synaptic weights are achieved at the array level, and a successful program-inhibiting operation is demonstrated in the synaptic array, achieving a learning accuracy of 79.84% on the Canadian Institute for Advanced Research (CIFAR)-10 dataset. Furthermore, an efficient self-curing method is proposed to improve the endurance of the FeFET array by tenfold, utilizing the punch-through current inherent to the device. Low-frequency noise spectroscopy is employed to quantitatively evaluate the curing efficiency of the proposed self-curing method. The results of this study provide a method to fabricate and operate reliable synaptic FeFET arrays, thereby paving the way for further development of ferroelectric-based neuromorphic computing.

Synergistic improvement of sensing performance in ferroelectric transistor gas sensors using remnant polarization.

Gaseous pollutants, including nitrogen oxides, pose a severe threat to ecosystems and human health; therefore, developing reliable gas-sensing systems to detect them is becoming increasingly important. Among the various options, metal-oxide-based gas sensors have attracted attention due to their capability for real-time monitoring and large response. In particular, in the field of materials science, there has been extensive research into controlling the morphological properties of metal oxides. However, these approaches have limitations in terms of controlling the response, sensitivity, and selectivity after the sensing material is deposited. In this study, we propose a novel method to improve the gas-sensing performance by utilizing the remnant polarization of ferroelectric thin-film transistor (FeTFT) gas sensors. The proposed FeTFT gas sensor has IGZO and HZO as the conducting channel and ferroelectric layer, respectively. It is demonstrated that the response and sensitivity of FeTFT gas sensors can be modulated by engineering the polarization of the ferroelectric layer. The amount of reaction sites in IGZO, including electrons and oxygen vacancy-induced negatively charged oxygen, is changed depending on upward and downward polarization. The results of this study provide an essential foundation for further development of gas sensors with tunable sensing properties.

Comprehensive and accurate analysis of the working principle in ferroelectric tunnel junctions using low-frequency noise spectroscopy.

Recently, ferroelectric tunnel junctions (FTJs) have gained extensive attention as possible candidates for emerging memory and synaptic devices for neuromorphic computing. However, the working principles of FTJs remain controversial despite the importance of understanding them. In this study, we demonstrate a comprehensive and accurate analysis of the working principles of a metal-ferroelectric-dielectric-semiconductor stacked FTJ using low-frequency noise (LFN) spectroscopy. In contrast to resistive random access memory, the 1/f noise of the FTJ in the low-resistance state (LRS) is approximately two orders of magnitude larger than that in the high-resistance state (HRS), indicating that the conduction mechanism in each state differs significantly. Furthermore, the factors determining the conduction of the FTJ in each state are revealed through a systematic investigation under various conditions, such as varying the electrical bias, temperature, and bias stress. In addition, we propose an efficient method to decrease the LFN of the FTJ in both the LRS and HRS using high-pressure forming gas annealing.

Proposition of deposition and bias conditions for optimal signal-to-noise-ratio in resistor- and FET-type gas sensors.

In the field of gas sensor studies, most researchers are focusing on improving the response of the sensors to detect a low concentration of gas. However, factors that make a large response, such as abundant or strong adsorption sites, also work as a source of noise, resulting in a trade-off between response and noise. Thus, the response alone cannot fully evaluate the performance of sensors, and the signal-to-noise-ratio (SNR) should additionally be considered to design gas sensors with optimal performance. In this regard, thin-film-type sensing materials are good candidates thanks to their moderate response and noise level. In this paper, we investigate the effects of radio frequency (RF) sputtering power for deposition of sensing materials on the SNR of resistor- and field-effect transistor (FET)-type gas sensors fabricated on the same Si wafer. In the case of resistor-type gas sensors, the deposition conditions that improve the response also worsen the noise either by increasing the scattering at the bulk or damaging the interface of the sensing material. Among resistor-type gas sensors with sensing materials deposited with different RF powers, a sensor with low noise shows the largest SNR despite its small response. However, the noise of FET-type gas sensors is not affected by changes in RF power and thus there is no trade-off between response and noise. The results reveal different noise sources depending on the deposition conditions of the sensing material, and provide design guidelines for resistor- and FET-type gas sensors considering noise for optimal performance.

On-Chip Training Spiking Neural Networks Using Approximated Backpropagation With Analog Synaptic Devices.

Hardware-based spiking neural networks (SNNs) inspired by a biological nervous system are regarded as an innovative computing system with very low power consumption and massively parallel operation. To train SNNs with supervision, we propose an efficient on-chip training scheme approximating backpropagation algorithm suitable for hardware implementation. We show that the accuracy of the proposed scheme for SNNs is close to that of conventional artificial neural networks (ANNs) by using the stochastic characteristics of neurons. In a hardware configuration, gated Schottky diodes (GSDs) are used as synaptic devices, which have a saturated current with respect to the input voltage. We design the SNN system by using the proposed on-chip training scheme with the GSDs, which can update their conductance in parallel to speed up the overall system. The performance of the on-chip training SNN system is validated through MNIST data set classification based on network size and total time step. The SNN systems achieve accuracy of 97.83% with 1 hidden layer and 98.44% with 4 hidden layers in fully connected neural networks. We then evaluate the effect of non-linearity and asymmetry of conductance response for long-term potentiation (LTP) and long-term depression (LTD) on the performance of the on-chip training SNN system. In addition, the impact of device variations on the performance of the on-chip training SNN system is evaluated.

Pruning for Hardware-Based Deep Spiking Neural Networks Using Gated Schottky Diode as Synaptic Devices.

Deep learning represents state-of-the-art results in various machine learning tasks, but for applications that require real-time inference, the high computational cost of deep neural networks becomes a bottleneck for the efficiency. To overcome the high computational cost of deep neural networks, spiking neural networks (SNN) have been proposed. Herein, we propose a hardware implementation of the SNN with gated Schottky diodes as synaptic devices. In addition, we apply L1 regularization for connection pruning of the deep spiking neural networks using gated Schottky diodes as synap-tic devices. Applying L1 regularization eliminates the need for a re-training procedure because it prunes the weights based on the cost function. The compressed hardware-based SNN is energy efficient while achieving a classification accuracy of 97.85% which is comparable to 98.13% of the software deep neural networks (DNN).

Effect of Word-Line Bias on Linearity of Multi-Level Conductance Steps for Multi-Layer Neural Networks Based on NAND Flash Cells.

NAND flash memory which is mature technology has great advantage in high density and great storage capacity per chip because cells are connected in series between a bit-line and a source-line. Therefore, NAND flash cell can be used as a synaptic device which is very useful for a high-density synaptic array. In this paper, the effect of the word-line bias on the linearity of multi-level conductance steps of the NAND flash cell is investigated. A 3-layer perceptron network (784×200×10) is trained by a suitable weight update method for NAND flash memory using MNIST data set. The linearity of multi-level conductance steps is improved as the word line bias increases from Vth -0.5 to Vth +1 at a fixed bit-line bias of 0.2 V. As a result, the learning accuracy is improved as the word-line bias increases from Vth -0.5 to Vth+1.

Gated Schottky Diode-Type Synaptic Device with a Field-Plate Structure to Reduce the Forward Current.

A gated Schottky diode with a field-plate structure is proposed and investigated as a new low-power synaptic device to suppress the forward current of the Schottky diode. In a hardware-based neural network, unwanted forward current can flow through gated Schottky diode-type synaptic devices during integration operations, possibly causing a malfunction of the neural network and increasing the power consumption. By adopting a field-plate structure, a virtual pn junction to suppress the forward current of the Schottky diode is formed in the poly-Si active layer. As a result, the unwanted forward current of the gated Schottky diode is successfully reduced to less than 1 pA/µm.

Emerging memory technologies for neuromorphic computing.

In this paper, we reviewed the recent trends on neuromorphic computing using emerging memory technologies. Two representative learning algorithms used to implement a hardware-based neural network are described as a bio-inspired learning algorithm and software-based learning algorithm, in particular back-propagation. The requirements of the synaptic device to apply each algorithm were analyzed. Then, we reviewed the research trends of synaptic devices to implement an artificial neural network.

Associated with Differences in Sexual Risk-Taking Behaviors Among Migrants in South Korea.

We investigated influential factors on differences in sexual risk-taking among homosexual migrants. The data used in this paper are based on the survey and medical examination for migrants' sexual behaviors that was carried out by the Korea Federation for HIV/AIDS Prevention in 2011-2013 on participants living in South Korea. Among 1141 migrants, homosexuals were 0.54 times less likely to use condom than heterosexuals. Homosexuals were 2.93 times more likely to be infected with sexually transmitted diseases (STDs) than heterosexuals. Among 250 homosexual migrants, those who preferred risky sexual intercourse were 0.19 times less likely to use a condom than heterosexual migrants. Those who have a fixed sexual partner were 0.35 times less likely to be infected with HIV than their counterparts. Administrative programs for STDs prevention of migrants should be focused on their sexual risk-taking, which were limited to casual partnership, unprotected sex, and previous contraction of sexual diseases.

Vinpocetine inhibits NF-kappaB-dependent inflammation via an IKK-dependent but PDE-independent mechanism.

Inflammation is a hallmark of many diseases, such as atherosclerosis, chronic obstructive pulmonary disease, arthritis, infectious diseases, and cancer. Although steroids and cyclooxygenase inhibitors are effective antiinflammatory therapeutical agents, they may cause serious side effects. Therefore, developing unique antiinflammatory agents without significant adverse effects is urgently needed. Vinpocetine, a derivative of the alkaloid vincamine, has long been used for cerebrovascular disorders and cognitive impairment. Its role in inhibiting inflammation, however, remains unexplored. Here, we show that vinpocetine acts as an antiinflammatory agent in vitro and in vivo. In particular, vinpocetine inhibits TNF-alpha-induced NF-kappaB activation and the subsequent induction of proinflammatory mediators in multiple cell types, including vascular smooth muscle cells, endothelial cells, macrophages, and epithelial cells. We also show that vinpocetine inhibits monocyte adhesion and chemotaxis, which are critical processes during inflammation. Moreover, vinpocetine potently inhibits TNF-alpha- or LPS-induced up-regulation of proinflammatory mediators, including TNF-alpha, IL-1beta, and macrophage inflammatory protein-2, and decreases interstitial infiltration of polymorphonuclear leukocytes in a mouse model of TNF-alpha- or LPS-induced lung inflammation. Interestingly, vinpocetine inhibits NF-kappaB-dependent inflammatory responses by directly targeting IKK, independent of its well-known inhibitory effects on phosphodiesterase and Ca(2+) regulation. These studies thus identify vinpocetine as a unique antiinflammatory agent that may be repositioned for the treatment of many inflammatory diseases.