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Capacitive pressure sensors based on porous structures have been extensively explored for various applications because their sensing performance is superior to that of conventional polymer sensors. However, it is challenging to develop sufficiently sensitive pressure sensors with linearity over a wide pressure range owing to the trade-off between linearity and sensitivity. This study demonstrates a novel strategy for the fabrication of a pressure sensor consisting of stacked carbon nanotubes (CNTs) and polydimethylsiloxane. With the addition of carbon nanotubes, the structure is linearly compressed due to the reinforced mechanical properties, thereby resulting in high linearity. Additionally, the percolation effect is boosted by the CNTs having a high dielectric constant, thus improving the sensitivity. The pressure sensor exhibits linear sensitivity ( R 2 = 0.991) in the medium-pressure range (10-100 kPa). Furthermore, it delivers excellent performance with a fast response time (∼60 ms), in conjunction with high repeatability, reproducibility, and reliability (5 and 50 kPa/1000 cycles). The fabricated sensors are applied in wearable devices to monitor finger bending and detect finger motions in real time with high precision. The large-area sensor is integrated with a neural network to accurately recognize the sitting posture on a plane, thereby demonstrating the wide-range detection performance.

The surface states, poor carrier life, and other native defects in GaN nanorods (NRs) limit their utilization in high-speed and large-gain ultraviolet (UV) photodetection applications. Making a hybrid structure is one of the finest strategies to overcome such impediments. In this work, a polypyrrole (Ppy)-poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS)/GaN NRs hybrid structure is introduced for self-powered UV photodetection applications. This hybrid structure yields high photodetection performance, while pristine GaN NRs showed negligible photodetection properties. The ability of the photodetector is further boosted by functionalizing the hybrid structure with Ag nanowires (NWs). The Ag NWs-functionalized hybrid structure exhibited a responsivity of 3.1 × 10 3 (A/W), detectivity of 3.19 × 10 14 Jones, and external quantum efficiency of 1.0 6 × 10 6 (%) under a UV illumination of λ = 382 nm. This high photoresponse is due to the huge photon absorption rising from the localized surface plasmonic effect of a Ag NWs network. Also, the Ag NWs significantly improved the rising and falling times, which were noted to be 0.20 and 0.21 s, respectively. The model band diagram was proposed with the assistance of X-ray photoelectron spectroscopy to explore the origin of the superior performance of the Ag NWs-decorated Ppy-PEDOT:PSS/GaN NRs photodetector. The proposed hybrid structure seems to be a promising candidate for the development of high-performance UV photodetectors.

Toward the successful development of artificial intelligence, artificial synapses based on resistive switching devices are essential ingredients to perform information processing in spiking neural networks. In neural processes, synaptic plasticity related to the history of neuron activity plays a critical role during learning. In resistive switching devices, it is barely possible to emulate both short-term plasticity and long-term plasticity due to the uncontrollable dynamics of the conductive filaments (CFs). Despite extensive effort to realize synaptic plasticity in such devices, it is still challenging to achieve reliable synaptic functions due to the overgrowth of CFs in a random fashion. Herein, we propose an organic resistive switching device with bio-realistic synaptic functions by adjusting the CF diffusive parameter. In the proposed device, complete synaptic plasticity provides the history-dependent change in the conductance. Moreover, the homeostatic feedback, which resembles the biological process, regulates CF growth in our device, which enhances the reliability of synaptic plasticity. This novel concept for realizing synaptic functions in organic resistive switching devices may provide a physical platform to advance the fundamental understanding of learning and memory mechanisms and develop a variety of neural circuits and neuromorphic systems that can be linked to artificial intelligence and next-generation computing paradigm.

In this study, the resistive switching and synaptic properties of a complementary metal-oxide semiconductor-compatible Ti/ a -BN/Si device are investigated for neuromorphic systems. A gradual change in resistance is observed in a positive SET operation in which Ti diffusion is involved in the conducting path. This operation is extremely suitable for synaptic devices in hardware-based neuromorphic systems. The isosurface charge density plots and experimental results confirm that boron vacancies can help generate a conducting path, whereas the conducting path generated by a Ti cation from interdiffusion forms is limited. A negative SET operation causes a considerable decrease in the formation energy of only boron vacancies, thereby increasing the conductivity in the low-resistance state, which may be related to RESET failure and poor endurance. The pulse transient characteristics, potentiation and depression characteristics, and good retention property of eight multilevel cells also indicate that the positive SET operation is more suitable for a synaptic device owing to the gradual modulation of conductance. Moreover, pattern recognition accuracy is examined by considering the conductance values of the measured data in the Ti/ a -BN/Si device as the synaptic part of a neural network. The linear and symmetric synaptic weight update in a positive SET operation with an incremental voltage pulse scheme ensures higher pattern recognition accuracy.

Triplet-triplet annihilation upconversion (TTA-UC) has recently drawn widespread interest for its capacity to harvest low-energy photons and to broaden the absorption spectra of photonic devices, such as solar cells. Although conceptually promising, effective integration of TTA-UC materials into practical devices has been difficult due to the diffusive and anoxic conditions required in TTA-UC host media. Of the solid-state host materials investigated, rubbery polymers facilitate the highest TTA-UC efficiency. To date, however, their need for long-term oxygen protection has limited rubbery polymers to rigid film architectures that forfeit their intrinsic flexibility. This study introduces a new multilayer thin-film architecture, in which scalable solution processing techniques are employed to fabricate flexible, photostable, and efficient TTA-UC thin films containing layers of oxygen barrier and host polymers. This breakthrough material design marks a crucial advance toward TTA-UC integration within rigid and flexible devices alike. Moreover, it introduces new opportunities in unexplored applications such as anticounterfeiting. Soft lithography is incorporated into the film fabrication process to pattern TTA-UC host layers with a broad range of high-resolution microscale designs, and superimposing host layers with customized absorption, emission, and patterning ultimately produces proof-of-concept anticounterfeiting labels with advanced excitation-dependent photoluminescent security features.

In this paper, we present a synapse function using analog resistive-switching behaviors in a SiN x -based memristor with a complementary metal-oxide-semiconductor compatibility and expandability to three-dimensional crossbar array architecture. A progressive conductance change is attainable as a result of the gradual growth and dissolution of the conducting path, and the series resistance of the AlO y layer in the Ni/SiN x /AlO y /TiN memristor device enhances analog switching performance by reducing current overshoot. A continuous and smooth gradual reset switching transition can be observed with a compliance current limit (>100 μA), and is highly suitable for demonstrating synaptic characteristics. Long-term potentiation and long-term depression are obtained by means of identical pulse responses. Moreover, symmetric and linear synaptic behaviors are significantly improved by optimizing pulse response conditions, which is verified by a neural network simulation. Finally, we display the spike-timing-dependent plasticity with the multipulse scheme. This work provides a possible way to mimic biological synapse function for energy-efficient neuromorphic systems by using a conventional passive SiN x layer as an active dielectric.