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Electrolyte‐gated synaptic transistors (EGSTs) have attracted considerable attention as synaptic devices owing to their adjustable conductance, low power consumption, and multi‐state storage capabilities. To demonstrate high‐density EGST arrays, 2D materials are recommended owing to their excellent electrical properties and ultrathin profile. However, widespread implementation of 2D‐based EGSTs has challenges in achieving large‐area channel growth and finding compatible nanoscale solid electrolytes. This study demonstrates large‐scale process‐compatible, all‐solid‐state EGSTs utilizing molybdenum disulfide (MoS2) channels grown through chemical vapor deposition (CVD) and sub‐30 nm organic‐inorganic hybrid electrolyte polymers synthesized via initiated chemical vapor deposition (iCVD). The iCVD technique enables precise modulation of the hydroxyl group density in the hybrid matrix, allowing the modulation of proton conduction, resulting in adjustable synaptic performance. By leveraging the tunable iCVD‐based hybrid electrolyte, the fabricated EGSTs achieve remarkable attributes: a wide on/off ratio of 109, state retention exceeding 103, and linear conductance updates. Additionally, the device exhibits endurance surpassing 5 × 104 cycles, while maintaining a low energy consumption of 200 fJ/spike. To evaluate the practicality of these EGSTs, a subset of devices is employed in system‐level simulations of MNIST handwritten digit recognition, yielding a recognition rate of 93.2%. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) technology has attracted tremendous interest in the medical community, from image analysis to lesion diagnosis. However, progress in medical AI is hampered by a lack of available medical image datasets and labor‐intensive labeling processes. Here, it is demonstrated that a large number of annotated, realistic chest X‐ray images can be generated using a state‐of‐the‐art generative adversarial network (GAN) that exploits noise produced by stochastic in‐memory computing of memristor crossbar arrays. Memristors based on polymer film with high thermal resistance can increase the stochasticity of the tunneling distance for randomly ruptured conductive filaments via excessive Joule heating, thus generating true random numbers required for creating naturally diverse images in GAN. Using StyleGAN2‐adaptive discriminator augmentation (ADA), high‐quality chest X‐ray images with and without pneumothorax are successfully augmented while maintaining a good Frechet inception distance score. The results provide a cost‐effective solution for preparing privacy‐sensitive medical images and labeling to develop innovative medical AI algorithms. [ABSTRACT FROM AUTHOR]

Internet‐of‐things (IoT) edge devices with a memristive neuromorphic system can more effectively enhance daily lives. However, cyberattacks remain critical concerns for smart IoT edge devices that process a vast body of information via networks. Herein, a highly secure neuromorphic system is reported, which can be implemented using a physically unclonable function (PUF) that exploits the high entropy achieved via the stochastic switching of a poly(1,3,5‐trivinyl‐1,3,5‐trimethyl cyclotrisiloxane) (pV3D3)‐based memristor. The excellent insulating property of pV3D3 enhances the stochasticity of the tunneling distance for randomly ruptured Cu filaments. The pV3D3 memristor‐based PUF (pV3D3‐PUF) achieves near‐ideal 50% averages for uniformity and uniqueness, excellent reliability under conditions of mechanical stress and water immersion, and reconfigurability‐bolstering security without additional hardware. Using stochastic in‐memory computing, the pV3D3‐PUF shows resilience to machine learning attacks. Furthermore, a cryptography protocol is demonstrated, which enables artificial intelligence service implementation without security issues for PUF‐integrated pV3D3 memristor‐based neuromorphic systems. [ABSTRACT FROM AUTHOR]

As the use of artificial intelligence (AI) soars, the development of novel neuromorphic computing is demanding because of the disadvantages of the von Neumann architecture. Furthermore, extensive research on electrochemical metallization (ECM) memristors as synaptic cells have been carried out toward a linear conductance update for online learning applications. In most cases, however, a conductance distribution change over time has not been studied as a major issue, giving less consideration to inference‐only computing accelerators based on offline learning. Herein, organic–inorganic bilayer stacking for synaptic unit cells using poly(1,3,5‐trivinyl‐1,3,5‐trimethyl cyclotrisiloxane) (pV3D3) and Al2O3 thin films is suggested, showing highly enhanced reliability for offline learning. The bilayer structure achieves better reliability and control of the analog resistive switching and synaptic functions, respectively, through the guided formation of conductive filaments via tip‐enhanced electric fields. In addition, 5‐bit multilevel states achieve long‐term stability (>104 s) following an in‐depth study on conductance‐level stability. Finally, a device‐to‐system‐level simulation is performed by building a convolutional neural network (CNN) based on the hybrid devices. This highlighted the significance of multilevel states in fully connected layers. It is believed that the study provides a practical approach to using ECM‐based memristors for inference‐only neural network accelerators. [ABSTRACT FROM AUTHOR]

In an article published in Advanced Functional Materials, Sung‐Yool Choi, Byung Chul Jang, and their colleagues present a method for generating realistic chest X-ray images using a generative adversarial network (GAN) and stochastic in-memory computing of memristor crossbar arrays. The researchers demonstrate that this cost-effective solution has the potential to generate privacy-sensitive medical images and aid in the development of innovative medical AI algorithms. The article provides a large number of annotated chest X-ray images and highlights the benefits of their approach for machine learning-based medical diagnosis. [Extracted from the article]

Graphene oxide (GO) doping and reduction allow for physicochemical property modification to suit practical application needs. Herein, the challenge of simultaneous low‐thermal‐budget heteroatom doping of GO and its reduction in ambient air is addressed through the synthesis of B‐doped reduced GO (B@rGO) by flash irradiation of boric acid loaded onto a GO support with intense pulsed light (IPL). The effects of light power and number of shots on the in‐depth sequential doping and reduction mechanisms are investigated by ex situ X‐ray photoelectron spectroscopy and direct millisecond‐scale temperature measurements (temperature >1600 °C, < 10‐millisecond duration, ramping rate of 5.3 × 105 °C s−1). Single‐flash IPL allows the large‐scale synthesis of substantially doped B@rGO (≈3.60 at% B) to be realized with a thermal budget 106‐fold lower than that of conventional thermal methods, and the prepared material with abundant B active sites is employed for highly sensitive and selective room‐temperature NO2 sensing. Thus, this work showcases the great potential of optical annealing for millisecond‐scale ultrafast reduction and heteroatom doping of GO in ambient air, which allows the tuning of multiple physicochemical GO properties. [ABSTRACT FROM AUTHOR]

Abstract: Edges of 2D transition metal dichalcogenides (TMDs) are well known as highly reactive sites, thus researchers have attempted to maximize the edge site density of 2D TMDs. In this work, metal‐organic framework (MOF) templates are introduced to synthesize few‐layered WS2 nanoplates (a lateral dimension of ≈10 nm) confined in Co, N‐doped hollow carbon nanocages (WS2_Co‐N‐HCNCs), for highly sensitive NO2 gas sensors. WS2 precursors are assembled in the surface cavity of Co‐based zeolite imidazole framework (ZIF‐67) and subsequent pyrolysis produced WS2_Co‐N‐HCNCs. During the pyrolysis, the carbonized ZIF‐67 are doped by Co and N elements, and the growth of WS2 is effectively suppressed, creating few‐layered WS2 nanoplates functionalized Co‐N‐HCNCs. The WS2_Co‐N‐HCNCs exhibit outstanding NO2 sensing characteristics at room temperature, in terms of response (48.2% to 5 ppm), selectivity, response and recovery speed, and detection limit (100 ppb). These results are attributed to the enhanced adsorption and desorption kinetics of NO2 on abundant WS2 edges, confined in the gas permeable HCNCs. This work opens up an efficient way for the facile synthesis of edge abundant few‐layered TMDs combined with porous carbon matrix via MOF templating route, for applications relying on highly active sites. [ABSTRACT FROM AUTHOR]

Transition metal dichalcogenides (TMDs), such as molybdenum disulfide (MoS2) and tungsten disulfide (WS2), have been studied intensively in recent years due to wide range of potential applications. TMD gas sensors have been developed and intensively explored for their promising applications. In recent times, it has been reported that edge sites of TMDs can contribute to highly enhanced gas adsorption properties. Herein, superior room temperature gas sensing properties of WS2 edge functionalized carbon nanofibers (CNFs) with multiple tubular pores (WS2@MTCNFs) have been demonstrated. A copolymer-electrospinning route, which uses poly(styrene-acrylonitrile) as sacrificial templates and WS2 precursor containing poly(acrylonitrile) as carbon matrix, offered facile synthesis of CNFs having high gas permeability with single-layered WS2 edge-rich surface. As a result, WS2@MTCNFs based sensors exhibited notable gas response (15% at 1 ppm of NO2) at room temperature compared to pristine CNFs (2% at 1 ppm of NO2), which can be attributed to the synergistic effects that originated from enhanced surface area and open porosity with numerous elongated pore channels of MTCNFs as well as remarkably increased active spots on the surface from WS2 edge sites. [ABSTRACT FROM AUTHOR]

2D‐layered transition metal dichalcogenides (TMDCs) such as molybdenum disulfide (MoS2) are promising materials for next‐generation active matrix organic light‐emitting diode (AMOLED) display technology owing to their high mobility and large bandgap size. However, practical applications of TMDCs in driving circuits for flexible displays remain challenging because of the lack of high‐quality large‐area thin films and suitable fabrication processes. Here, millimeter‐scale large‐area bilayer or trilayer MoS2 thin films are synthesized through chemical vapor deposition (CVD) and an AMOLED driver circuit array consisting of bottom‐gate staggered CVD‐grown MoS2 thin‐film transistors is fabricated on a flexible polyimide substrate. The flexible driver circuit exhibits a stable switching and driving operation under tensile strain induced by a bending radius of 3.5 mm, showing field‐effect mobilities of up to ≈9 cm2 V−1 s−1, large ON‐state current density (up to ≈5 µA µm−1), and high ON/OFF‐state drain current ratio (maximum value of over 108) with an operating gate voltage below 10 V. The results demonstrate that MoS2 backplanes are among the promising candidates for next‐generation deformable and transparent AMOLED displays. A flexible active matrix organic light‐emitting diode (AMOLED) pixel driving‐circuit array on a flexible polyimide substrate is demonstrated, thus addressing the current limitations in the application of transition metal dichalcogenides because of the lack of high‐quality large‐area thin films and suitable fabrication processes. [ABSTRACT FROM AUTHOR]