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, Ti3C2Tx, 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.

Organic field-effect transistor (OFET) memory has received widespread attention due to its easy integration, precise charge modulation, and multi-level memory. However, the performance of organic memory still needs to be improved for its practical application, and the reported technologies are strongly dependent on an additional charge-trapping layer, which increases the complexity of the device. Here, we report a heterostructured vertical organic memory transistor, which uses a p/n semiconductor bulk heterojunction as a semiconductor layer without using any additional charge-trapping layers. The device exhibits a large memory window of 52 V, and the memory ratio reaches 10 5 through electrical operation. Benefiting from the formation of the p/n semiconductor interface and the nanometer-scale transmission length, under the stimulation of visible light, the device achieved a 58 V memory window, high memory ratio 10 5 , and retention characteristics of over 10 years, which is better than those of most reported optical organic memory devices. More interestingly, we found that as the level of the doping in the n-type semiconductor increased, the device could transform from nonvolatile memory to artificial synapse, which is associated with the morphology of a heterojunction structure. Hence, we demonstrate a novel technique to manufacture high-performance nonvolatile optoelectronic memory and artificial synapse, which shows great potential in OFET-based memory and neuromorphic devices.

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.