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Vertical transistors have attracted enormous attention in the next-generation electronic devices due to their high working frequency, low operation voltage and large current density, while a major scientific and technological challenge for high performance vertical transistor is to find suitable source electrode. Herein, an MXene material, Ti 3 C 2 T x , is introduced as source electrode of organic vertical transistors. The porous MXene films take the advantage of both partially shielding effect of graphene and the direct modulation of the Schottky barrier at the mesh electrode, which significantly enhances the ability of gate modulation and reduces the subthreshold swing to 73 mV/dec. More importantly, the saturation of output current which is essential for all transistor-based applications but remains a great challenge for vertical transistors, is easily achieved in our device due to the ultra-thin thickness and native oxidation of MXene, as verified by finite-element simulations. Finally, our device also possesses great potential for being used as wide-spectrum photodetector with fast response speed without complex material and structure design. This work demonstrates that MXene as source electrode offers plenty of opportunities for high performance vertical transistors and photoelectric devices. The modulation of Schottky barrier, which dominates the carrier injection in vertical organic field-effect transistors, strongly depends on the source electrode. Here, Chen et al. utilize MXene as a source electrode, achieving a subthreshold swing down to 73 mv/dec and a large gate control ability.

Selective attention is an efficient processing strategy to allocate computational resources for pivotal optical information. However, the hardware implementation of selective visual attention in conventional intelligent system is usually bulky and complex along with high computational cost. Here, programmable ferroelectric bionic vision hardware to emulate the selective attention is proposed. The tunneling effect of photogenerated carriers are controlled by dynamic variation of energy barrier, enabling the modulation of memory strength from 9.1% to 47.1% without peripheral storage unit. The molecular polarization of ferroelectric P(VDF-TrFE) layer enables a single device not only multiple nonvolatile states but also the implementation of selective attention. With these ferroelectric devices are arrayed together, UV light information can be selectively recorded and suppressed the with high current decibel level. Furthermore, the device with positive polarization exhibits high wavelength dependence in the image attention processing, and the fabricated ferroelectric sensory network exhibits high accuracy of 95.7% in the pattern classification for multi-wavelength images. This study can enrich the neuromorphic functions of bioinspired sensing devices and pave the way for profound implications of future bioinspired optoelectronics. Selective attention is an efficient processing strategy to allocate computational resources for pivotal optical information. Here, the authors propose a bionic vision hardware to emulate the behavior, showing a potential in image classification.

Devices with sensing-memory-computing capability for the detection, recognition and memorization of real time sensory information could simplify data conversion, transmission, storage, and operations between different blocks in conventional chips, which are invaluable and sought-after to offer critical benefits of accomplishing diverse functions, simple design, and efficient computing simultaneously in the internet of things (IOT) era. Here, we develop a self-powered vertical tribo-transistor (VTT) based on MXenes for multi-sensing-memory-computing function and multi-task emotion recognition, which integrates triboelectric nanogenerator (TENG) and transistor in a single device with the simple configuration of vertical organic field effect transistor (VOFET). The tribo-potential is found to be able to tune ionic migration in insulating layer and Schottky barrier height at the MXene/semiconductor interface, and thus modulate the conductive channel between MXene and drain electrode. Meanwhile, the sensing sensitivity can be significantly improved by 711 times over the single TENG device, and the VTT exhibits excellent multi-sensing-memory-computing function. Importantly, based on this function, the multi-sensing integration and multi-model emotion recognition are constructed, which improves the emotion recognition accuracy up to 94.05% with reliability. This simple structure and self-powered VTT device exhibits high sensitivity, high efficiency and high accuracy, which provides application prospects in future human-mechanical interaction, IOT and high-level intelligence. Designing efficient sensing-memory-computing systems remains a challenge. Here, the authors propose a self-powered vertical tribo-transistor based on MXenes to implement the multi-sensing-memory-computing function and the interaction of multisensory integration.

Ferroelectric semiconductors have the advantages of switchable polarization ferroelectric field regulation and semiconductor transport characteristics, which are highly promising in ferroelectric transistors and nonvolatile memory. However, it is difficult to prepare a Sn-based perovskite film with both robust ferroelectric and semiconductor properties. Here, by doping with 2-methylbenzimidazole, Sn-based perovskite [93.3 mol% (FA 0.86 Cs 0.14 )SnI 3 and 6.7 mol% PEA 2 SnI 4 ] semiconductor films are transformed into ferroelectric semiconductor films, owing to molecular reconfiguration. The reconfigured ferroelectric semiconductors exhibit a high remanent polarization ( P r ) of 23.2 μC/cm 2 . The emergence of ferroelectricity can be ascribed to the hydrogen bond enhancement after imidazole molecular doping, and then the spatial symmetry breaks causing the positive and negative charge centers to become non-coincident. Remarkably, the transistors based on perovskite ferroelectric semiconductors have a low subthreshold swing of 67 mv/dec, which further substantiates the superiority of introducing ferroelectricity. This work has developed a method to realize Sn-based ferroelectric semiconductor films for electronic device applications. The authors observe the emergence of ferroelectricity in Sn-based perovskite [93.3 mol% (FA0.86Cs0.14)SnI3 and 6.7 mol% PEA2SnI4] semiconductor films doped with 2-methylbenzimidazole, ascribing to the hydrogen bond enhancement after imidazole molecular doping.

The non-volatile spontaneous ferroelectric polarization field serves as a cornerstone for applying ferroelectric materials in electronic devices, yet it is frequently mitigated by charge trapping at defect sites. Achieving an effective transition between ferroelectric polarization and charge trapping is challenging due to the inherent opposition of the two mechanisms and the uncontrollable charge trapping types in ferroelectric materials. Here, we realized a polarity-dependent ferroelectric transition in two-dimensional ferroelectric heterojunction transistor by integrating a hybrid organic-inorganic ferroelectric layer embedded with electron trapping sites. Through theoretical calculations and experimental validation, we demonstrate a ferroelectric manifestation and elimination mechanism based on the polarity of the semiconductor layer. The electron-majority n-type semiconductor exhibits charge trapping behavior, while the electron-minority p-type transistor exhibits the ferroelectric control mechanism. Leveraging the mechanism transition, our bipolar heterojunction transistor enables synergistic heterogeneous control of non-volatile memory and volatile synaptic weight modulation within a single bipolar ferroelectric transistor. Based on the experimentally extracted parameters from the transistors, the device-informed simulation achieves a recognition accuracy of 92.9% and a 20.7-fold improvement in training efficiency of the transfer learning network. The authors demonstrate a two-dimensional ferroelectric heterojunction transistor that exploits polarity-dependent transitions between ferroelectricity and charge trapping, enabling both memory and synaptic functions, and enhancing AI training efficiency.

Two‐dimensional (2D) van der Waals heterostructure (vdWH)‐based floating gate devices show great potential for next‐generation nonvolatile and multilevel data storage memory. However, high program voltage induced substantial energy consumption, which is one of the primary concerns, hinders their applications in low‐energy‐consumption artificial synapses for neuromorphic computing. In this study, we demonstrate a three‐terminal floating gate device based on the vdWH of tin disulfide (SnS2), hexagonal boron nitride (h‐BN), and few‐layer graphene. The large electron affinity of SnS2 facilitates a significant reduction in the program voltage of the device by lowering the hole‐injection barrier across h‐BN. Our floating gate device, as a nonvolatile multilevel electronic memory, exhibits large on/off current ratio (~105), good retention (over 104 s), and robust endurance (over 1000 cycles). Moreover, it can function as an artificial synapse to emulate basic synaptic functions. Further, low energy consumption down to ~7 picojoule (pJ) can be achieved owing to the small program voltage. High linearity (<1) and conductance ratio (~80) in long‐term potentiation and depression (LTP/LTD) further contribute to the high pattern recognition accuracy (~90%) in artificial neural network simulation. The proposed device with attentive band engineering can promote the future development of energy‐efficient memory and neuromorphic devices. A three‐terminal floating gate device consisted of tin disulfide (SnS2), hexagonal boron nitride (h‐BN), and few‐layer graphene is demonstrated based on the attentive band engineering. It can function as a nonvolatile multilevel electronic memory featuring the excellent characteristics. For the emulation of artificial synapse, multiple synaptic functions can be realized with the low energy consumption.

Quasi-2D tin-based perovskites are promising p-type semiconductors due to their thermodynamic stability and suppressed ion migration tendencies. However, the competitive growth of low- and high-dimensional phases leads to pronounced structural disorder, increased defect density, and poor crystallographic orientation, thereby restricting charge transport. Here, phenethylammonium thiocyanate (PEASCN) is incorporated into the precursor to promote the preferential formation of PEA 2 FA n -1 Sn n I 3 n -1 SCN 2 ( n  = 2) templates. Substituting formamidinium iodide (FAI) with formamidinium formate (FAHCOO) and ammonium iodide (NH 4 I) suppresses the uncontrollable growth of 3D FASnI 3 at room temperature, enabling precise crystallization control. These low-dimensional templates guide the growth of high-dimensional phases upon annealing, yielding vertically oriented films with reduced defects. The fabricated field-effect transistors exhibit mobility up to 43 cm 2  V −1 s −1 and an on/off ratio exceeding 10 8 , alongside nearly negligible hysteresis and enhanced stability. These results demonstrate a viable approach for regulating crystallization kinetics and realizing high-performance, stable tin-based perovskite devices. Wu et al. report the sequential crystallization in quasi-2D tin-based perovskites, achieved by preferential formation of low-dimensional templates and delayed 3D growth, yielding vertically oriented and low-defect films for field-effect transistors with high mobility, negligible hysteresis, and improved stability.

Memristors have emerged as a transformative technology in the realm of electronic devices, offering unique advantages such as fast switching speeds, low power consumption, and the ability to sensor-memory-compute. The applications span across non-volatile memory, neuromorphic computing, hardware security, and beyond, prompting memristors to become a versatile solution for next-generation computing and data storage systems. Despite enormous potential of memristors, the transition from laboratory prototypes to large-scale applications is challenging in terms of material stability, device reproducibility, and array scalability. This review systematically explores recent advancements in high-performance memristor technologies, focusing on performance enhancement strategies through material engineering, structural design, pulse protocol optimization, and algorithm control. We provide an in-depth analysis of key performance metrics tailored to specific applications, including non-volatile memory, neuromorphic computing, and hardware security. Furthermore, we propose a co-design framework that integrates device-level optimizations with operational-level improvements, aiming to bridge the gap between theoretical models and practical implementations.

The emergence of sliding ferroelectricity is found in non‐ferroelectric two‐dimensional materials, which brings novel ferroelectric phenomena and expands the potential for advancing ferroelectric devices. Experimental studies have largely focused on sliding ferroelectricity with fixed twist angles owing to the limitations of preparation methods with controlled angles. However, how to modulate the ferroelectric properties in the sliding materials is still challenging. In this work, the out‐of‐plane ferroelectric properties of typical bilayer MoS2/WS2 heterostructure are reported by precisely controlling twist angles. The experimental results demonstrate that the second‐harmonic generation response, indicative of symmetry breaking, decreases as the twist angle increases. In addition, the switching voltage of ferroelectric polarization exhibits the opposite trend with increasing the twist angle. According to experimental studies and theoretical calculations, the tunability of ferroelectric properties arises from the distortion of polar symmetry regions induced by Moiré patterns at different twist angles. Furthermore, the ferroelectric semiconductor field‐effect transistors yield the twist angles dependent electrical properties, achieving a large ferroelectric memory window of ≈14 V. The study opens the door to significantly modulating the sliding ferroelectricity via designing twist angles, which will enrich the framework of twistronics and expand the promising applications in the emerging sliding ferroelectric devices. The study reports the precise control of interlayer twist angles in the bilayer MoS2/WS2 heterostructure. The ferroelectric properties of MoS2/WS2 heterostructure can be significantly modulated by designing twist angles, resulting from the distortion of polar symmetric regions induced by Moiré patterns. The study opens the door to manipulate sliding ferroelectric properties, which will bring emerging ferroelectric twistronics devices.

To resolve the problems of deep convolutional neural network models with many parameters and high memory resource consumption, a lightweight network-based algorithm for building detection of Minnan folk light synthetic aperture radar (SAR) images is proposed. Firstly, based on the rotating target detection algorithm R-centernet, the Ghost ResNet network is constructed to reduce the number of model parameters by replacing the traditional convolution in the backbone network with Ghost convolution. Secondly, a channel attention module integrating width and height information is proposed to enhance the network’s ability to accurately locate salient regions in folk light images. Content-aware reassembly of features (CARAFE) up-sampling is used to replace the deconvolution module in the network to fully incorporate feature map information during up-sampling to improve target detection. Finally, the constructed dataset of rotated and annotated light and shadow SAR images is trained and tested using the improved R-centernet algorithm. The experimental results show that the improved algorithm improves the accuracy by 3.8%, the recall by 1.2% and the detection speed by 12 frames/second compared with the original R-centernet algorithm.