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.

On-Chip Annealing Using Embedded Micro-Heater for Highly Sensitive and Selective Gas Detection.

The demand for gas sensing systems that enable fast and precise gas recognition is growing rapidly. However, substantial challenges arise from the complex fabrication process of sensor arrays, time-consuming data transmission to an external processor, and high energy consumption in multi-stage data processing. In this study, a gas sensing system using on-chip annealing for fast and power-efficient gas detection is proposed. By utilizing a micro-heater embedded in the gas sensor, the sensing material of adjacent sensors in the same substrate can be easily varied without further fabrication steps. The response to oxidizing gas is constrained in metal oxide (MOX) sensing material with small grain sizes, as the depletion width of grain cannot extend beyond the grain size during the gas reaction. On the other hand, the response to reducing gases and humidity, which decrease the depletion width, is less affected by grain sizes. A readout circuit integrating a differential amplifier and dual FET-type gas sensors effectively emphasizes the response to oxidizing gases by canceling the response to reducing gases and humidity. The selective on-chip annealing method is applicable to various MOX sensing materials, demonstrating its potential for application in commercial fields due to its simplicity and expandability.

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 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.

Transducer-Aware Hydroxy-Rich-Surface Indium Oxide Gas Sensor for Low-Power and High-Sensitivity NO2 Gas Sensing.

Low-power metal oxide (MOX)-based gas sensors are widely applied in edge devices. To reduce power consumption, nanostructured MOX-based sensors that detect gas at low temperatures have been reported. However, the fabrication process of these sensors is difficult for mass production, and these sensors are lack uniformity and reliability. On the other hand, MOX film-based gas sensors have been commercialized but operate at high temperatures and exhibit low sensitivity. Herein, commercially advantageous highly sensitive, film-based indium oxide sensors operating at low temperatures are reported. Ar and O2 gases are simultaneously injected during the sputtering process to form a hydroxy-rich-surface In2O3 film. Conventional indium oxide (In2O3) films (A0) and hydroxy-rich indium oxide films (A1) are compared using several analytical techniques. A1 exhibits a work function of 4.92 eV, larger than that of A0 (4.42 eV). A1 exhibits a Debye length 3.7 times longer than that of A0. A1 is advantageous for gas sensing when using field effect transistors (FETs) and resistors as transducers. Because of the hydroxy groups present on the surface of A1, A1 can react with NO2 gas at a lower temperature (∼100 °C) than A0 (180 °C). Operando diffuse reflectance infrared Fourier transform spectrometry (DRIFTS) shows that NO2 gas is adsorbed to A1 as nitrite (NO2-) at 100 °C and nitrite and nitrate (NO3-) at 200 °C. After NO2 is adsorbed as nitrate, the sensitivity of the A1 sensor decreases and its low-temperature operability is compromised. On the other hand, when NO2 is adsorbed only as nitrite, the performance of the sensor is maintained. The reliable hydroxy-rich FET-type gas sensor shows the best performance compared to that of the existing film-based NO2 gas sensors, with a 2460% response to 500 ppb NO2 gas at a power consumption of 1.03 mW.

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.

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.

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.

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.

Humidity-Sensitive Field Effect Transistor with In2O3 Nanoparticles as a Sensing Layer.

In this work, we investigate the humidity-sensing performance on a humidity-sensitive p-channel field effect transistor (FET) having a floating-gate (FG) and a control-gate (CG) placing horizontally each other. A sensing layer is formed onto a part of the CG and the O/N/O stack over the FG by inkjet-printing process. The printed ink is composed of indium oxide (In2O3. nanoparticles and dimethylformamide (HCON(CH3)2) as solvent. DC/Pulsed measurements are carried out by switching chamber ambience between dry and humid N2 at 25 °C. Pulsed measurement effectively alleviates the ID drift of the device. When the device is exposed to humidity, the |ID| is appreciably decreased in the p-channel FET-type sensor, since H2O molecules act as an electron donor. The sensitivity of the sensor increases with increasing relative humidity up to about 68% and decreases with further increasing relative humidity.

Observation of physisorption in a high-performance FET-type oxygen gas sensor operating at room temperature.

Oxygen (O2) sensors are needed for monitoring environment and human health. O2 sensing at low temperature is required, but studies are lacking. Here we report, for the first time, that the performance of a field effect transistor (FET)-type O2 sensor operating at 25 °C was improved greatly by a physisorption sensing mechanism. The sensing material was platinum-doped indium oxide (Pt-In2O3) nanoparticles formed by an inkjet printer. The FET-type sensor showed excellent repeatability under a physisorption mechanism and showed much better sensing performance than a resistor-type sensor fabricated on the same wafer at 25 °C. The sensitivity of the sensor increased with increasing Pt concentration up to ∼10% and decreased with further increasing Pt concentration. When the sensing temperature reached 140 °C, the sensing mechanism of the sensor changed from physisorption to chemisorption. Interestingly, the pulse pre-bias before the read bias affected chemisorption but had no effect on physisorption.