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.

Toward Ideal Low-Frequency Noise in Monolayer CVD MoS2 FETs: Influence of van der Waals Junctions and Sulfur Vacancy Management.

The pursuit of sub-1-nm field-effect transistor (FET) channels within 3D semiconducting crystals faces challenges due to diminished gate electrostatics and increased charge carrier scattering. 2D semiconductors, exemplified by transition metal dichalcogenides, provide a promising alternative. However, the non-idealities, such as excess low-frequency noise (LFN) in 2D FETs, present substantial hurdles to their realization and commercialization. In this study, ideal LFN characteristics in monolayer MoS2 FETs are attained by engineering the metal-2D semiconductor contact and the subgap density of states (DOS). By probing non-ideal contact resistance effects using CuS and Au electrodes, it is uncovered that excess contact noise in the high drain current (ID) region can be substantially reduced by forming a van der Waals junction with CuS electrodes. Furthermore, thermal annealing effectively mitigates sulfur vacancy-induced subgap density of states (DOS), diminishing excess noise in the low ID region. Through meticulous optimization of metal-2D semiconductor contacts and subgap DOS, alignment of 1/f noise with the pure carrier number fluctuation model is achieved, ultimately achieving the sought-after ideal LFN behavior in monolayer MoS2 FETs. This study underscores the necessity of refining excess noise, heralding improved performance and reliability of 2D electronic devices.

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.

Reconfigurable Manipulation of Oxygen Content on Metal Oxide Surfaces and Applications to Gas Sensing.

Oxygen vacancies and adsorbed oxygen species on metal oxide surfaces play important roles in various fields. However, existing methods for manipulating surface oxygen require severe settings and are ineffective for repetitive manipulation. We present a method to manipulate the amount of surface oxygen by modifying the oxygen adsorption energy by electrically controlling the electron concentration of the metal oxide. The surface oxygen control ability of the method is verified using first-principles calculations based on density functional theory (DFT), X-ray photoelectron spectroscopy (XPS), and electrical resistance analysis. The presented method is implemented by fabricating oxide thin film transistors with embedded microheaters. The method can reconfigure the oxygen vacancies on the In2O3, SnO2, and IGZO surfaces so that specific chemisorption dominates. The method can selectively increase oxidizing (e.g., NO and NO) and reducing gas (e.g., H2S, NH3, and CO) reactions by electrically controlling the metal oxide surface to be oxygen vacancy-rich or adsorbed oxygen species-rich. The proposed method is applied to gas sensors and overcomes their existing limitations. The method makes the sensor insensitive to one gas (e.g., H2S) in mixed-gas environments (e.g., NO2+H2S) and provides a linear response (R2 = 0.998) to the target gas (e.g., NO2) concentration within 3 s. We believe that the proposed method is applicable to applications utilizing metal oxide surfaces.

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.

Recovery of off-state stress-induced damage in FET-type gas sensor using self-curing method.

The need for high-performance gas sensors is driven by concerns over indoor and outdoor air quality, and industrial gas leaks. Due to their structural diversity, vast surface area, and geometric tunability, metal oxides show significant potential for the development of gas sensing systems. Despite the fact that several previous reports have successfully acquired a suitable response to various types of target gases, it remains difficult to maintain the reliability of metal oxide-based gas sensors. In particular, the degradation of the sensor platform under repetitive operation, such as off-state stress (OSS) causes significant reliability issues. We investigate the impact of OSS on the gas sensing performances, including response, low-frequency noise, and signal-to-noise ratio of horizontal floating-gate field-effect-transistor (FET)-type gas sensors. The 1/f noise is increased after the OSS is applied to the sensor because the gate oxide is damaged by hot holes. Therefore, the SNR of the sensor is degraded by the OSS. We applied a self-curing method based on a PN-junction forward current at the body-drain junction to repair the damaged gate oxide and improve the reliability of the sensor. It has been demonstrated that the SNR degradation caused by the OSS can be successfully recovered by the self-curing method.

In-Memory-Computed Low-Frequency Noise Spectroscopy for Selective Gas Detection Using a Reducible Metal Oxide.

Concerns about indoor and outdoor air quality, industrial gas leaks, and medical diagnostics are driving the demand for high-performance gas sensors. Owing to their structural variety and large surface area, reducible metal oxides hold great promise for constructing a gas-sensing system. While many earlier reports have successfully obtained a sufficient response to various types of target gases, the selective detection of target gases remains challenging. In this work, a novel method, low-frequency noise (LFN) spectroscopy is presented, to achieve selective detection using a single FET-type gas sensor. The LFN of the sensor is accurately modeled by considering the charge fluctuation in both the sensing material and the FET channel. Exposure to different target gases produces distinct corner frequencies of the power spectral density that can be used to achieve selective detection. In addition, a 3D vertical-NAND flash array is used with the fast Fourier transform method via in-memory-computing, significantly improving the area and power efficiency rate. The proposed system provides a novel and efficient method capable of selectively detecting a target gas using in-memory-computed LFN spectroscopy and thus paving the way for the further development in gas sensing systems.

Highly Selective and Low-Power Carbon Monoxide Gas Sensor Based on the Chain Reaction of Oxygen and Carbon Monoxide to WO3.

Carbon monoxide (CO) poisoning can easily occur in industrial and domestic settings, causing headaches, loss of consciousness, or death from overexposure. Commercially available CO gas sensors consume high power (typically 38 mW), whereas low-power gas sensors using nanostructured materials with catalysts lack reliability and uniformity. A low-power (1.8 mW @ 392 °C), sensitive, selective, reliable, and practical CO gas sensor is presented. The sensor adopts floated WO3 film as a sensing material to utilize the unique reaction of lattice oxide of WO3 with CO gas. The sensor locally modulates the electron concentration in the WO3 film, allowing O2 and CO gases to react primarily in different sensing areas. Electrons generated by the CO gas reaction can be consumed for O2 gas adsorption in a remote area, and this promotes the additional reaction of CO gas, boosting sensitivity and selectivity. The proposed sensor exhibits a 39.5 times higher response than the conventional resistor-type gas sensor fabricated on the same wafer. As a proof of concept, sensors with In2O3 film are fabricated, and the proposed sensor platform shows no advantage in detecting CO gas. Fabrication of the proposed sensor is reproducible and inexpensive due to conventional silicon-based processes, making it attractive for practical applications.

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.

Effect of charge storage engineering on the NO2 gas sensing properties of a WO3 FET-type gas sensor with a horizontal floating-gate.

In this paper, we investigate the effects of charge storage engineering (CSE) on the NO2 gas sensing properties such as response, recovery, and sensitivity of a FET-type gas sensor with a horizontal floating-gate (FG) having tungsten trioxide (WO3) as a sensing layer. When the FET transducer is set at an erase state (ΔVth = -2 V), the holes injected into the FG by Fowler-Nordheim (F-N) tunneling increase the electron concentration at the WO3-passivation layer interface. Accordingly, an oxidizing gas, NO2, can take more electrons from WO3, which increases the change in the FG voltage (ΔVFG) by a factor of 2.4. Also, the recovery speed of the sensor in the erase state can be improved by applying pre-bias (Vpre) which is larger than the read bias (Vread). As the carriers in the WO3 film that can interact with NO2 increase by the excess holes stored in the FG by the erase operation, the sensitivity of the sensor also increases 3.2 times. The effects of CSE on various sensing performances are explained using energy band diagrams.

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.