Development of Bio-Voltage Operated Humidity-Sensory Neurons Comprising Self-Assembled Peptide Memristors.

Biomimetic humidity sensors offer a low-power approach for respiratory monitoring in early lung-disease diagnosis. However, balancing miniaturization and energy efficiency remains challenging. This study addresses this issue by introducing a bioinspired humidity-sensing neuron comprising a self-assembled peptide nanowire (NW) memristor with unique proton-coupled ion transport. The proposed neuron shows a low Ag+ activation energy owing to the NW and redox activity of the tyrosine (Tyr)-rich peptide in the system, facilitating ultralow electric-field-driven threshold switching and a high energy efficiency. Additionally, Ag+ migration in the system can be controlled by a proton source owing to the hydrophilic nature of the phenolic hydroxyl group in Tyr, enabling the humidity-based control of the conductance state of the memristor. Furthermore, a memristor-based neuromorphic perception neuron that can encode humidity signals into spikes is proposed. The spiking characteristics of this neuron can be modulated to emulate the strength-modulated spike-frequency characteristics of biological neurons. A three-layer spiking neural network with input neurons comprising these highly tunable humidity perception neurons shows an accuracy of 92.68% in lung-disease diagnosis. This study paves the way for developing bioinspired self-assembly strategies to construct neuromorphic perception systems, bridging the gap between artificial and biological sensing and processing paradigms.

Energy-efficient transistors: suppressing the subthreshold swing below the physical limit.

With the miniaturization of silicon-based electronic components, power consumption is becoming a fundamental issue for micro-nano electronic circuits. The main reason for this is that the scaling of the supply voltage in the ultra-large-scale integrated circuit cannot keep up with the shrinking of the characteristic size of conventional transistors due to the physical limit termed "Boltzmann Tyranny", in which a gate voltage of at least 60 mV is required to modulate the drain current by one order of magnitude. Accordingly, to solve this problem, several new transistor architectures have been designed to reduce the subthreshold swing (SS) to lower than the fundamental limitation, thus lowering the supply voltage and reducing the power consumption. In this review, we first analytically formulate the SS, summarize the methods for reducing the SS, and propose four new transistor concepts, including tunnelling field-effect transistor, negative capacitance field-effect transistor, impact ionization field-effect transistor, and cold source field-effect transistor. Then, we review their physical mechanisms and optimization methods and consider the potential and drawbacks of these four new transistors. Finally, we discuss the challenges encountered in the investigation of these steep-slope transistors and present the future outlook.

Reconfigurable 2D WSe2 -Based Memtransistor for Mimicking Homosynaptic and Heterosynaptic Plasticity.

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.

Synaptic transistors and neuromorphic systems based on carbon nano-materials.

Carbon-based materials possessing a nanometer size and unique electrical properties perfectly address the two critical issues of transistors, the low power consumption and scalability, and are considered as a promising material in next-generation synaptic devices. In this review, carbon-based synaptic transistors were systematically summarized. In the carbon nanotube section, the synthesis of carbon nanotubes, purification of carbon nanotubes, the effect of architecture on the device performance and related carbon nanotube-based devices for neuromorphic computing were discussed. In the graphene section, the synthesis of graphene and its derivative, as well as graphene-based devices for neuromorphic computing, was systematically studied. Finally, the current challenges for carbon-based synaptic transistors were discussed.

Configurable multi-state non-volatile memory behaviors in Ti3C2 nanosheets.

MXenes have drawn considerable attention in both academia and industry due to their attractive properties, such as a combination of metallic conductivity and surface hydrophilicity. However, to the best of our knowledge, the potential use of MXenes in non-volatile resistive random access memories (RRAMs) has rarely been reported. In this paper, we first demonstrated a RRAM device with MXene (Ti3C2) as the active component. The Ti3C2-based RRAM exhibited typical bipolar switching behavior, long retention characteristics, low SET voltage, good mechanical stability and excellent reliability. By adjusting different compliance currents in the SET process, multi-state information storage was achieved. The charge trapping assisting hopping process is considered to be the main mechanism of resistive switching for this fabricated Ti3C2-based RRAM, which was verified by conductive atomic force microscopy (C-AFM) and Kelvin probe force microscopy (KPFM). Moreover, this flexible Ti3C2-based RRAM, with good mechanical stability and long retention properties, was successfully fabricated on a plastic substrate. Ti3C2-based RRAMs may open the door to additional applications and functionalities, with high potential for application in flexible electronics.

Near-Infrared Annihilation of Conductive Filaments in Quasiplane MoSe2 /Bi2 Se3 Nanosheets for Mimicking Heterosynaptic Plasticity.

It is desirable to imitate synaptic functionality to break through the memory wall in traditional von Neumann architecture. Modulating heterosynaptic plasticity between pre- and postneurons by another modulatory interneuron ensures the computing system to display more complicated functions. Optoelectronic devices facilitate the inspiration for high-performance artificial heterosynaptic systems. Nevertheless, the utilization of near-infrared (NIR) irradiation to act as a modulatory terminal for heterosynaptic plasticity emulation has not yet been realized. Here, an NIR resistive random access memory (RRAM) is reported, based on quasiplane MoSe2 /Bi2 Se3 heterostructure in which the anomalous NIR threshold switching and NIR reset operation are realized. Furthermore, it is shown that such an NIR irradiation can be employed as a modulatory terminal to emulate heterosynaptic plasticity. The reconfigurable 2D image recognition is also demonstrated by an RRAM crossbar array. NIR annihilation effect in quasiplane MoSe2 /Bi2 Se3 nanosheets may open a path toward optical-modulated in-memory computing and artificial retinal prostheses.

Infrared-Sensitive Memory Based on Direct-Grown MoS2 -Upconversion-Nanoparticle Heterostructure.

Photonic memories as an emerging optoelectronic technology have attracted tremendous attention in the past few years due to their great potential to overcome the von Neumann bottleneck and to improve the performance of serial computers. Nowadays, the decryption technology for visible light is mature in photonic memories. Nevertheless, near-infrared (NIR) photonic memristors are less progressed. Herein, an NIR photonic memristor based on MoS2 -NaYF4 :Yb3+ , Er3+ upconversion nanoparticles (UCNPs) nanocomposites is designed. Under excitation by 980 nm NIR light, the UCNPs show emissions well overlapping with the absorption band of the MoS2 nanosheets. The heterostructure between the MoS2 and the UCNPs acting as excitons generation/separation centers remarkably improves the NIR-light-controlled memristor performance. Furthermore, in situ conductive atomic force microscopy is employed to elucidate the photo-modulated memristor mechanism. This work provides novel opportunities for NIR photonic memory that holds promise in future multifunctional robotics and electronic eyes.

Preparation and Thermoelectric Properties of Co-Doped ZnO Synthesized by Sol-Gel.

Zinc oxide (ZnO) has attracted increasing attention as one of the most promising n-type thermo-electric materials, but its practice use was limited by high thermal conductivity and low electrical conductivity. Therefore, we herein prepared Co-doped ZnO nanoparticles by sol-gel method and then compressed nanoparticles into bulk materials through spark plasma sintering. The thermo-electric properties, including electrical conductivity, Seebeck coefficient, thermal conductivity, and ZT value, have been investigated. We found that the substitution of Co2+ causes the decrease of bandgap and the increase of carrier concentration, thus the improvement of electrical conductivity. At the same time, the Co-induced lattice distortion and nanoparticles reduce the thermal conductivity by shortening the mean free path (MFP) of the phonons. The resultant ZT is 0.037 for Zn0.9Co0.1O, which is more than 23-fold higher than that of the pure ZnO samples.

In situ precipitation of Te nanoparticles in p-type BiSbTe and the effect on thermoelectric performance.

Through zone melting method, a certain amount of Te nano precipitations were in situ generated in the p-type BiSbTe matrix because of the addition of graphene. Both the microstructure and thermoelectric performance were investigated. Increased carrier concentration was obtained to improve the electrical performance, and the lattice thermal conductivity was simultaneously lowered about 25% by Te nano precipitations as phonon scattering centers. Consequently, an optimization of the thermoelectric figure-of-merit ZT between 375 and 550 K was achieved.