Highly Sensitive Oxygen Sensing Characteristics Observed in IGZO Based Gasistor in a Mixed Gas Ambient at Room Temperature

Advances in memristive gas sensors: A review

With the development of gas sensing technology, traditional semiconductor-based gas sensors are difficult to meet higher performance requirements. To this end, the essence of gas sensor performance improvement depends on the innovation of gas-sensing mechanism. Gas sensors based on the memristor structure (gasistors) have been proposed in recent years, which brings new research ideas for further gas sensors development. Here, we demonstrate a comprehensive overview of the gasistor structures, fabrication, performance, applications and mechanisms. Gasistor structures are compatible with memristors and gas sensors, ranging from typical sandwich structures to those with modified electrodes and porous resistive layers aimed to balance resistive switching and gas sensing functions. Meanwhile, the fabrication process involves common materials such as metals and metal oxides, while novel materials are being explored to optimize performance. It is worth noting that gasistors exhibit unique performance including room temperature sensing, variable gas selectivity, tunable recovery and self-heating against humidity. In applications, apart from gas monitoring, gasistors are used as gas-triggered switches for accident recording, and as olfactory synapses for learning memory. The gas-sensing mechanism is respectively elucidated on the molecular and atomic scales, breaking through the surface conductivity-type mechanism. Finally, the prospects and challenges of gasistors are discussed.

From materials to applications: a review of research on artificial olfactory memory

Olfactory memory forms the basis for biological perception and environmental adaptation. Advancing artificial intelligence to replicate this biological perception as artificial olfactory memory is essential. The widespread use of various robotic systems, intelligent wearable devices, and artificial olfactory memories modeled after biological olfactory memory is anticipated. This review paper highlights current developments in the design and application of artificial olfactory memory, using examples from materials science, gas sensing, and storage systems. These innovations in gas sensing and neuromorphic technology represent the cutting edge of the field. They provide a robust scientific foundation for the study of intelligent bionic devices and the development of hardware architectures for artificial intelligence. Artificial olfaction will pave the way for future advancements in intelligent recognition by progressively enhancing the level of integration, understanding of mechanisms, and application techniques of machine learning algorithms.

Improving the Thermal Stability of Indium Oxide n-Type Field-Effect Transistors by Enhancing Crystallinity through Ultrahigh-Temperature Rapid Thermal Annealing

Ultrathin indium oxide films show great potential as channel materials of complementary metal oxide semiconductor back-end-of-line transistors due to their high carrier mobility, smooth surface, and low leakage current. However, it has severe thermal stability problems (unstable and negative threshold voltage shifts at high temperatures). In this paper, we clarified how the improved crystallinity of indium oxide by using ultrahigh-temperature rapid thermal O2 annealing could reduce donor-like defects and suppress thermal-induced defects, drastically enhancing thermal stability. Not only does more crystalline indium oxide depict the high stability of threshold voltage in stringent high-temperature test environments and under positive bias, but it also shows much less degradation under forming gas annealing than as-deposited transistors. Furthermore, we also successfully solved the channel length-dependent threshold voltage problem, which is often observed in oxide transistors, by suppressing defects induced by the metal deposition process and metal doping.

IGZO/WO3-x-Heterostructured Artificial Optoelectronic Synaptic Devices Mimicking Image Segmentation and Motion Capture

Currently, artificial neural networks (ANNs) based on memristors are limited to recognizing static images of objects when simulating human visual system, preventing them from performing high-dimensional information perception, and achieving more complex biomimetic functions is subject to certain limitations. In this work, indium gallium zinc oxide (IGZO)/tungsten oxide (WO3-x)-heterostructured artificial optoelectronic synaptic devices mimicking image segmentation and motion capture exhibiting high-performance optoelectronic synaptic responses are proposed and demonstrated. Upon electrical and optical stimulations, the device shows a variety of fundamental and advanced electrical and optical synaptic plasticity. Most importantly, outstanding and repeatable linear synaptic weight changes are attained by the developed memristor. By taking advantage of the notable linear synaptic weight changes, ANNs have been constructed and successfully utilized to demonstrate two applications in the field of computer vision, including image segmentation and object tracking. The accuracy attained by the memristor-based ANNs is similar to that of the computer algorithms, while its power has been significantly reduced by 105 orders of magnitude. With successful emulations of the human brain reactions when observing objects, the demonstrated memristor and related ANNs can be effectively utilized in constructing artificial optoelectronic synaptic devices and show promising potential in emulating human visual perception.

Low-Power Consumption IGZO Memristor-Based Gas Sensor Embedded in an Internet of Things Monitoring System for Isopropanol Alcohol Gas

Low-power-consumption gas sensors are crucial for diverse applications, including environmental monitoring and portable Internet of Things (IoT) systems. However, the desorption and adsorption characteristics of conventional metal oxide-based gas sensors require supplementary equipment, such as heaters, which is not optimal for low-power IoT monitoring systems. Memristor-based sensors (gasistors) have been investigated as innovative gas sensors owing to their advantages, including high response, low power consumption, and room-temperature (RT) operation. Based on IGZO, the proposed isopropanol alcohol (IPA) gas sensor demonstrates a detection speed of 105 s and a high response of 55.15 for 50 ppm of IPA gas at RT. Moreover, rapid recovery to the initial state was achievable in 50 μs using pulsed voltage and without gas purging. Finally, a low-power circuit module was integrated for wireless signal transmission and processing to ensure IoT compatibility. The stability of sensing results from gasistors based on IGZO has been demonstrated, even when integrated into IoT systems. This enables energy-efficient gas analysis and real-time monitoring at ~0.34 mW, supporting recovery via pulse bias. This research offers practical insights into IoT gas detection, presenting a wireless sensing system for sensitive, low-powered sensors.

A Review of Structure Engineering of Strain-Tolerant Architectures for Stretchable Electronics

Stretchable electronics possess significant advantages over their conventional rigid counterparts and boost game-changing applications such as bioelectronics, flexible displays, wearable health monitors, etc. It is, nevertheless, a formidable task to impart stretchability to brittle electronic materials such as silicon. This review provides a concise but critical discussion of the prevailing structural engineering strategies for achieving strain-tolerant electronic devices. Not only the more commonly discussed lateral designs of structures such as island-bridge, wavy structures, fractals, and kirigami, but also the less discussed vertical architectures such as strain isolation and elastoplastic principle are reviewed. Future opportunities are envisaged at the end of the paper.

Correlation between Sensing Accuracy and Read Margin of a Memristor-Based NO Gas Sensor Array Estimated by Neural Network Analysis

Memristor-based gas sensors (gasistors) have been considered as the most promising candidate for detecting NO gas suitable for neural network (NN) analysis. In this work, in order to solve an overfitting issue arising from the training data when using a single gasistor, which degrades the accuracy of NN, we here propose a metal-insulator-silicon (MIS)-structured Zr₃N₄-based gasistor array that results in an improvement in both the accuracy of the NN analysis and the efficiency of the operating power. As a result, the proposed gasistor array showed a decrease of epoch and a 2.5% improvement of prediction accuracy at room temperature compared to single cells with metal/insulator/metal (MIM) and MIS structures. These results imply that an array structure based on MIS can efficiently solve the overfitting issue by receiving multiple responses at once, compared to a single gas sensor that obtains one response per sensing.

Pd/Pt-Bimetallic-Nanoparticle-Doped In2O3 Hollow Microspheres for Rapid and Sensitive H2S Sensing at Low Temperature

H₂S is a poisonous gas that is widespread in nature and human activities. Its rapid and sensitive detection is essential to prevent it from damaging health. Herein, we report Pd- and Pt-bimetallic-nanoparticle-doped In₂O₃ hollow microspheres that are synthesized using solvothermal and in situ reduction methods for H₂S detection. The structure of as-synthesized 1 at% Pd/Pt-In₂O₃ comprises porous hollow microspheres assembled from In₂O₃ nanosheets with Pd and Pt bimetallic nanoparticles loaded on its surface. The response of 1 at% Pd/Pt-In₂O₃ to 5 ppm H₂S is 140 (70 times that of pure In₂O₃), and the response time is 3 s at a low temperature of 50 °C. In addition, it can detect trace H₂S (as low as 50 ppb) and has superior selectivity and an excellent anti-interference ability. These outstanding gas-sensing performances of 1 at% Pd/Pt-In₂O₃ are attributed to the chemical sensitization of Pt, the electronic sensitization of Pd, and the synergistic effect between them. This work supplements the research of In₂O₃-based H₂S sensors and proves that Pd- and Pt-bimetallic-doped In₂O₃ can be applied in the detection of H₂S.

Sensitivity Enhancement of Resistive Ethanol Gas Sensor by Optimized Sputtered-Assisted CuO Decoration of ZnO Nanorods

In this study, sputtered-assisted CuO-decorated ZnO nanorod (NR) gas sensors were fabricated for ethanol gas sensing studies. CuO nanoparticles have been successfully formed on ZnO nanorods by means of a physical process as the decorative metallic element. The amount of decoration affecting the sensor's performance has been optimized. Cu layers with different thicknesses of 5, 10, and 20 nm were deposited on hydrothermally grown ZnO NRs using the sputtering technique. Upon subsequent annealing, Cu was oxidized to CuO. The gas sensing studies revealed that the sensor with an initial Cu layer of 5 nm had the highest response to ethanol at 350 °C. The sensor also showed good selectivity, repeatability, and long-term stability. The enhanced ethanol sensing response of the optimized gas sensor is related to the formation of p-n heterojunction between p-type CuO and n-type ZnO and the presence of the optimal amount of CuO on the surface of ZnO NRs. The results presented in this study highlight the need for optimizing the amount of Cu deposition on the surface of ZnO NRs in order to achieve the highest response to ethanol gas.