Recently, conductive metal−organic frameworks (MOFs) as the active material have provided broad prospects for electronic device application. The positioning technologies for MOFs enable the fabrication of novel microstructures, which can modulate the morphology of the material and tune the properties for the targeted application. Herein, a template‐method is used to synthesize the hierarchical structure of MOF hybrid array (MHA) on copper mesh (MHA@Mesh) for flexible sensor. Finite element method (FEM) results indicate that the 3D hierarchical MHA@Mesh can mimic the micro/nanoscale structure of human skin, which enables an interlocking contact. MHA@Mesh‐based flexible sensor presents rapid response rate (<1 ms) and high sensitivity (up to 307 kPa−1) which is 20 times higher than that of MHA@Foil‐based sensor (15 kPa−1). The flexible pressure device could be applied to monitor the finger motion and human pulses. Moreover, the music recognition can be performed by integrating the MOFs hardware sensors with machine learning algorithms. Overall, this design concept of 3D hierarchical microarray structures demonstrates potential in the fields of wearable technologies and human–machine interfaces.

The mimicking of both homosynaptic and heterosynaptic plasticity using a high‐performance synaptic device is important for developing human‐brain–like neuromorphic computing systems to overcome the ever‐increasing challenges caused by the conventional von Neumann architecture. However, the commonly used synaptic devices (e.g., memristors and transistors) require an extra modulate terminal to mimic heterosynaptic plasticity, and their capability of synaptic plasticity simulation is limited by the low weight adjustability. In this study, a WSe2‐based memtransistor for mimicking both homosynaptic and heterosynaptic plasticity is fabricated. By applying spikes on either the drain or gate terminal, the memtransistor can mimic common homosynaptic plasticity, including spiking rate dependent plasticity, paired pulse facilitation/depression, synaptic potentiation/depression, and filtering. Benefitting from the multi‐terminal input and high adjustability, the resistance state number and linearity of the memtransistor can be improved by optimizing the conditions of the two inputs. Moreover, the device can successfully mimic heterosynaptic plasticity without introducing an extra terminal and can simultaneously offer versatile reconfigurability of excitatory and inhibitory plasticity. These highly adjustable and reconfigurable characteristics offer memtransistors more freedom of choice for tuning synaptic weight, optimizing circuit design, and building artificial neuromorphic computing systems.

As a kind of structurally designable, nanoscale controllable, and performance optimizable materials, metal-organic frameworks (MOFs), consisting of organic ligands and metal nodes, have been widely studied as promising materials in a lot of fields over the last two decades, such as sensors, drug delivery, gas separation and storage, catalysis, and so on. [1] However, compared to these mature applications, the adoption of MOFs in emerging areas, such as information storage and processing, is relatively less. [1c,2] In order to expand the applied range of MOFs in new areas, designing and synthesizing new structure MOFs with novel properties, such as conducting and semiconducting characteristics, was recently used. [1c,3] The synthesis of new structure MOFs will lead to the relative high experimental cost and more complex synthesis process. Therefore, expanding the application of the pre-existing MOFs with high performance in emerging areas may be a more effective choice. In this information explosion era, data storage and processing is one of the most significant research field, and various functional materials have been used to develop high performance information storage and processing devices. [4] Up to now, MOFs have been used as active layer for developing nonvolatile memory (NVM), especially resistive random access memory (RRAM), [1c,5] which has been identified as one of the most potential developing direction for future non volatile data storage technique. [6] Moreover, compared with bulk MOFs, 2D MOFs show more attractive performance because they have ultrathin thickness, larger specific surface area, and more highly accessible active sites, and have attracted lots of research interest in the field of sensors, energy conversion and storage, biomedicine, gas separation, and electronic devices. [7] In these 2D MOF materials, M-TCPP (M: metal; TCPP: tetrakis(4carboxyphenyl)porphyrin) nanosheets have been used in biological detection, photocatalysis, RRAM, and other fields due to their simple synthetic process, uniform size, and proper thickness. [8,9] Due to the low information communication rate between central processing unit and main memory, the traditional von Two-dimensional (2D) metal-organic frameworks (MOFs) are widely used in a variety of mature applications, including catalysis, drug delivery, and sensors. Based on their highly accessible active sites, 2D MOFs are expected to be good charge trapping elements. Using 2D MOF, Zn-TCPP (TCPP: tetrakis(4-carboxyphenyl)porphyrin), as charge trapping materials by a simple solution process, a three-terminal synaptic device which can realize the learning functions and signal transmission simultaneously is firstly fabricated. The as-fabricated synaptic device exhibits ambipolar charge carrier trapping performance, large current on /current off ratio (>10 3) and excellent endurance (500 cycle times). Moreover, the common biological synaptic behaviors, including postsynaptic current under different temperature, pulse duration time and pulse voltage, paired-pulse...

diffusive memristors, have been developed for neuromorphic computing systems. [1c,2] Drift memristors are devoted to demonstrating qualitative synaptic functionality and realizing precise multi-bit weight updates in ANNs, [2b,c,3] while diffusive memristors (DMs) are expected to play a crucial and indispensable role in artificial neuron, neuromorphic computing system, true random number generator and reservoir computing (RC) system due to their volatile features. [2a,c,3c,4] RC concept is developed from the recurrent neural network (RNN) which is one kind of ANNs with recurrent connections for dealing with the task with temporal information but usually is hard to train and needs a great deal of computational power due to the vanishing or exploding gradients problem. [4j,5] Compared to RNNs, RC networks exhibit relatively simple training algorithms and linear training processes because lots of connection weights in a RC network are fixed and only the weights connected to the output layer need the training process. In a typical RC network, the DMs are used as nodes (neurons) in the reservoir layer to perform the nonlinear transformation to temporal information and then update the reservoir state which will be analyzed by the "readout function" for generating the output. Specifically, the memristor based RC network has been developed and used for some temporal tasks, such as the classification/recognition of numbers and letters, and biological signal analysis. [4e,h-j] Besides, the DMs have recently been performed to fabricate the artificial nociceptors which are expected to be applied in the intelligent humanoid robots for protecting them from harm by sensing the external noxious stimuli (e.g., extreme temperatures, mechanical stress, and chemical molecules) and producing warning signals. [6] One of the most important features of nociceptors is threshold, that is, the electric pulses (warning signals) can only be triggered when the external stimulus exceeds the threshold value. [6a] Besides, the features of "no adaptation", "allodynia" and "hyperalgesia" are also very important. No adaptation refers to that the nociceptors don't produce adaptation to the experienced external stimuli. [6a] This feature is different from other receptors (e.g., tough, taste, and sight) whose sensitivity decreases (i.e., adaptation) after prolonged exposure to the external stimulus. The allodynia and hyperalgesia are all sensitization effects which mean that the The switching variability caused by intrinsic stochasticity of the ionic/atomic motions during the conductive filaments (CFs) formation process largely limits the applications of diffusive memristors (DMs), including artificial neurons, neuromorphic computing and artificial sensory systems. In this study, a DM device with improved device uniformity based on well-crystallized two-dimensional (2D) h-BN, which can restrict the CFs formation from three to two dimensions due to the high migration barrier of Ag + between h-BN interlayer, is developed. The BN-DM has potenti...

One of the most effective approaches to solving the current problem arising from the von Neumann bottleneck in this period of data proliferation is the development of intelligent devices that mimic the human learning process. Information sensing and processing/storage are considered to be the essential processes of learning. Therefore, high-performance sensors, memory/synaptic devices, and relevant intelligent artificial tactile perception systems are urgently needed. In this regard, innovative device concepts and emerging two-dimensional materials have recently received considerable attention. Herein, we discuss the development of MXenes for applications in tactile sensors, memristors, and artificial tactile perception systems. First, we summarize the structures, common properties, and synthesis and assembly techniques of MXenes. We then discuss the applications of MXenes in tactile sensors, memristors, and relevant neuromorphic-based artificial tactile perception systems along with the related working mechanisms. Finally, we present the challenges and prospects related to MXene synthesis, assembly, and application.