Moiré synaptic transistor with room-temperature neuromorphic functionality

IGZO/PVP Composite Nanofiber Neuromorphic Transistors with Optoelectronic Synapse Emulation and Reservoir Computing

Nanofiber neuromorphic transistors are regarded as promising candidates for mimicking brain-like learning and advancing high-performance computing. Composite nanofibers (CNFs) typically exhibit enhanced optoelectronic and mechanical properties. In this study, indium-gallium-zinc oxide (IGZO)/polyvinylpyrrolidone (PVP) CNFs were synthesized, and the neuromorphic transistor was integrated on both rigid and flexible substrates. The learning behavior, characterized by the transition from short-term plasticity (STP) to long-term plasticity, was achieved through photoelectric stimulation of the rigid neuromorphic transistor. The nonlinear STP was simulated by the flexible neuromorphic transistor through electrical pulses, matching effectively with a reservoir computing (RC) system. Hand gesture recognition with little energy consumption (49 pJ per reservoir state) and a maximum accuracy of 92.86% has been achieved by the RC system, proving the substantial potential of the IGZO/PVP CNF neuromorphic transistor for wearable intelligent processing tasks.

An emergent attractor network in a passive resistive switching circuit

Resistive memory devices feature drastic conductance change and fast switching dynamics. Particularly, nonvolatile bipolar switching events (set and reset) can be regarded as a unique nonlinear activation function characteristic of a hysteretic loop. Upon simultaneous activation of multiple rows in a crosspoint array, state change of one device may contribute to the conditional switching of others, suggesting an interactive network existing in the circuit. Here, we prove that a passive resistive switching circuit is essentially an attractor network, where the binary memory devices are artificial neurons while the pairwise voltage differences define an anti-symmetric weight matrix. An energy function is successfully constructed for this network, showing that every switching in the circuit would decrease the energy. Due to the nonvolatile hysteretic function, the energy change for bit flip in this network is thresholded, which is different from the classic Hopfield network. It allows more stable states stored in the circuit, thus representing a highly compact and efficient solution for associative memory. Network dynamics (towards stable states) and their modulations by external voltages have been demonstrated in experiment by 3-neuron and 4-neuron circuits.

Performance Limits and Advancements in Single 2D Transition Metal Dichalcogenide Transistor

Two-dimensional (2D) transition metal dichalcogenides (TMDs) allow for atomic-scale manipulation, challenging the conventional limitations of semiconductor materials. This capability may overcome the short-channel effect, sparking significant advancements in electronic devices that utilize 2D TMDs. Exploring the dimension and performance limits of transistors based on 2D TMDs has gained substantial importance. This review provides a comprehensive investigation into these limits of the single 2D-TMD transistor. It delves into the impacts of miniaturization, including the reduction of channel length, gate length, source/drain contact length, and dielectric thickness on transistor operation and performance. In addition, this review provides a detailed analysis of performance parameters such as source/drain contact resistance, subthreshold swing, hysteresis loop, carrier mobility, on/off ratio, and the development of p-type and single logic transistors. This review details the two logical expressions of the single 2D-TMD logic transistor, including current and voltage. It also emphasizes the role of 2D TMD-based transistors as memory devices, focusing on enhancing memory operation speed, endurance, data retention, and extinction ratio, as well as reducing energy consumption in memory devices functioning as artificial synapses. This review demonstrates the two calculating methods for dynamic energy consumption of 2D synaptic devices. This review not only summarizes the current state of the art in this field but also highlights potential future research directions and applications. It underscores the anticipated challenges, opportunities, and potential solutions in navigating the dimension and performance boundaries of 2D transistors.

Enhancing 2D Photonics and Optoelectronics with Artificial Microstructures

By modulating subwavelength structures and integrating functional materials, 2D artificial microstructures (2D AMs), including heterostructures, superlattices, metasurfaces and microcavities, offer a powerful platform for significant manipulation of light fields and functions. These structures hold great promise in high-performance and highly integrated optoelectronic devices. However, a comprehensive summary of 2D AMs remains elusive for photonics and optoelectronics. This review focuses on the latest breakthroughs in 2D AM devices, categorized into electronic devices, photonic devices, and optoelectronic devices. The control of electronic and optical properties through tuning twisted angles is discussed. Some typical strategies that enhance light-matter interactions are introduced, covering the integration of 2D materials with external photonic structures and intrinsic polaritonic resonances. Additionally, the influences of external stimuli, such as vertical electric fields, enhanced optical fields and plasmonic confinements, on optoelectronic properties is analysed. The integrations of these devices are also thoroughly addressed. Challenges and future perspectives are summarized to stimulate research and development of 2D AMs for future photonics and optoelectronics.

Thermally and Mechanically Stable Perovskite Artificial Synapse as Tuned by Phase Engineering for Efferent Neuromuscular Control

The doping of perovskites with mixed cations and mixed halides is an effective strategy to optimize phase stability. In this study, we introduce a cubic black phase perovskite CsyFA(1-y)Pb(BrxI(1-x))3 artificial synapse, using phase engineering by adjusting the cesium-bromide content. Low-bromine mixed perovskites are suitable to improve the electric pulse excitation sensitivity and stability of the device. Specifically, the low-bromine and low-cesium mixed perovskite (x = 0.15, y = 0.22) annealed at 373 K allows the device to maintain logic response even after 1000 mechanical flex/flat cycles. The device also shows good thermal stability up to temperatures of 333 K. We have demonstrated reflex-arc behavior with MCMHP synaptic units, capable of making sensory warnings at high frequency. This compositionally engineered, dual-mixed perovskite synaptic device provides significant potential for perceptual soft neurorobotic systems and prostheses.

High-Mobility Compensated Semimetals, Orbital Magnetization, and Umklapp Scattering in Bilayer Graphene Moiré Superlattices

Twist-controlled moiré superlattices (MSs) have emerged as a versatile platform for realizing artificial systems with complex electronic spectra. The combination of Bernal-stacked bilayer graphene (BLG) and hexagonal boron nitride (hBN) can give rise to an interesting MS, which has recently featured a set of unexpected behaviors, such as unconventional ferroelectricity and the electronic ratchet effect. Yet, the understanding of the electronic properties of BLG/hBN MS has, at present, remained fairly limited. Here, we combine magneto-transport and low-energy sub-THz excitation to gain insights into the properties of this MS. We demonstrate that the alignment between BLG and hBN crystal lattices results in the emergence of compensated semimetals at some integer fillings of the moiré bands, separated by van Hove singularities where the Lifshitz transition occurs. A particularly pronounced semimetal develops when eight holes reside in the moiré unit cell, where coexisting high-mobility electron and hole systems feature strong magnetoresistance reaching 2350% already at B = 0.25 T. Next, by measuring the THz-driven Nernst effect in remote bands, we observe valley splitting, indicating an orbital magnetization characterized by a strongly enhanced effective gv-factor of 340. Finally, using THz photoresistance measurements, we show that the high-temperature conductivity of the BLG/hBN MS is limited by electron-electron umklapp processes. Our multifaceted analysis introduces THz-driven magnetotransport as a convenient tool to probe the band structure and interaction effects in van der Waals materials and provides a comprehensive understanding of the BLG/hBN MS.

Paraffin-Enabled Superlattice Customization for a Photostimulated Gradient-Responsive Artificial Reflex Arc

The development of photostimulated-motion artificial reflex arcs - a neural circuit inspired by light-driven motion reflexes - holds significant promises for advancements in robotic perception, navigation, and motion control. However, the fabrication of such systems, especially those that accommodate multiple actions and exhibit gradient responses, remains challenging. Here, a gradient-responsive photostimulated-motion artificial reflex arc is developed by integrating a programmable and tunable photoreceptor based on folded MoS2 at different twist angles. The twisted folded bilayer MoS2 used as photoreceptors can be customized via the transfer technique using patternable paraffin, where the twist angle and fold-line could be controlled. The photoluminescence (PL) intensity is 3.7 times higher at a twist angle of 29° compared to that at 0°, showing a monotonically decreasing indirect bandgap. Through tunable interlayer carrier transport, photoreceptors fabricated using folded bilayer MoS2 at different twist angles demonstrate gradient response time, enabling the photostimulated-motion artificial reflex arc for multiaction responses. They are transformed to digital command flow and studied via machine learning to control the gestures of a robotic hand, showing a prototype of photostimulated gradient-responsive artificial reflex arcs for the first time. This work provides a unique idea for developing intelligent soft robots and next-generation human-computer interfaces.