Bioinspired artificial haptic neuron system has received much attention in the booming artificial intelligence industry for its broad range of high-impact applications such as personal healthcare monitoring, electronic skins, and human-machine interfaces. An artificial haptic neuron system is designed by integrating a piezoresistive sensor and a Nafion-based memristor for the first time in this paper. The piezoresistive sensor serves as a sensory receptor to transform mechanical stimuli into electric signals, and the Nafion-based memristor serves as the synapse to further process the information. The pyramid-structured sensor exhibits excellent sensitivity (6.7 × 10 7 kPa −1 in 1-5 kPa and 3.8 × 10 5 kPa −1 in 5-50 kPa) and durability (>7000 cycles), while the memristor realizes fundamental synaptic functions under low power consumption (10-200 pJ) and remains stable for over 10 4 consecutive tests. The integrated system can detect tactile stimuli encoded with temporal information, such as the count, frequency, duration and speed of the external force. As a proof-of-concept, English characters recognition with high accuracy can be achieved on the system under a supervised learning method. This work shows promising potential in bioinspired sensing systems owing to the high performance, excellent durability, and simple fabrication procedure.

Metal-organic framework (MOF) nanosheets have attracted significant interests for sensing, electrochemical, and catalytic applications. Most significantly, 2D MOF with highly accessible sites on the surface is expected to be applicable in data storage. Here, the memory device is first demonstrated by employing M-TCPP (TCPP: tetrakis(4-carboxyphenyl) porphyrin, M: metal) as resistive switching (RS) layer. The as-fabricated resistive random access memory (RRAM) devices exhibit a typical electroforming free bipolar switching characteristic with on/off ratio of 10 3 , superior retention, and reliability performance. Furthermore, the time-dependent RS behaviors under constant voltage stress of 2D M-TCPP-based RRAMs are systematically investigated. The properties of the percolated conducting paths are revealed by the Weibull distribution by collecting the measured turn-on time. The multilevel information storage state can be gotten by setting a series of compliance current. The charge trapping assisted hopping is proposed as operation principle of the MOF-based RRAMs which is further confirmed by atomic force microscopy at electrical modes. The research is highly relevant for practical operation of 2D MOF nanosheet-based RRAM, since the time widths, magnitudes of pulses, and multilevel-data storage can be potentially set.

Phototunable biomaterial‐based resistive memory devices and understanding of their underlying switching mechanisms may pave a way toward new paradigm of smart and green electronics. Here, resistive switching behavior of photonic biomemory based on a novel structure of metal anode/carbon dots (CDs)‐silk protein/indium tin oxide is systematically investigated, with Al, Au, and Ag anodes as case studies. The charge trapping/detrapping and metal filaments formation/rupture are observed by in situ Kelvin probe force microscopy investigations and scanning electron microscopy and energy‐dispersive spectroscopy microanalysis, which demonstrates that the resistive switching behavior of Al, Au anode‐based device are related to the space‐charge‐limited‐conduction, while electrochemical metallization is the main mechanism for resistive transitions of Ag anode‐based devices. Incorporation of CDs with light‐adjustable charge trapping capacity is found to be responsible for phototunable resistive switching properties of CDs‐based resistive random access memory by performing the ultraviolet light illumination studies on as‐fabricated devices. The synergistic effect of photovoltaics and photogating can effectively enhance the internal electrical field to reduce the switching voltage. This demonstration provides a practical route for next‐generation biocompatible electronics.

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.

A multi-state information storage state could be achievedviaa configurable SET process with non-volatile devices based on Ti3C2nanosheets.

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...

of stimulation will transfer the shortterm memory into long-term memory. To achieve artificial synaptic recognition for learning and memory in electronic devices, concomitant plasticity with different timescale is essential. As a result, the short-term plasticity (STP) modulates the transient dynamical efficacy during the synaptic transmission, while the longterm plasticity (LTP) shows the stabilizing effect by the given stimulation. [10][11][12][13] Therefore, the emulated synaptic plasticity of STP and LTP render themselves supportive to the sophisticated cognitive function and adaptive behavior pattern.The emphasis on neuroinspired computing so far has been predominantly in electrical stimulation-induced resistance state switching in phase change memories, [14,15] memristors, [16][17][18][19][20][21][22][23][24] as well as transistor-based memories. [25][26][27][28][29][30] In contrast with the existing electrical interconnect power loss as well as the limitation in trigger selectivity and spatially confinement inherently from the computing by electric signal, emerging optical stimulation based synaptic devices can tune the synaptic plasticity enormously by photons with low-power and high-efficiency. Therefore, the photonic synapse architecture is considered to be more favorable in handling the von Neumann bottleneck. [31][32][33][34] In addition, photonic synapse based on Parallel information storage coupled with storage density is a major focus for non-volatile memory devices to achieve neuromorphic computing that can work at low power. In this regard, a photoactive charge-trapping medium consisting of inorganic heteronanosheets for the fabrication of a synaptic transistor is demonstrated. This synaptic device senses and responds to near-infrared (NIR) light signals and mimics the memorization and dynamic forgetting process due to the reversible nature of photogenerated charge interaction. Device-level synaptic evolutions from short-term plasticity to long-term plasticity, paired pulse facilitation, and paired pulse depression are realized with light modulation on the weight update terminal. To understand the underlying mechanism of the synaptic behavior under NIR signals, systematic analysis is carried out using in situ atomic force microscopy based electrical techniques. With its photoactive architecture, this information processing analogue is validated for visual object recognition, which paves the way for implementing NIR-controlled neuromorphic computing.