Enhancing Layer-Engineered Interlayer Exciton Emission and Valley Polarization in van der Waals Heterostructures via Strain.

Layer-engineered interlayer excitons from heterostructures of transition-metal dichalcogenides (TMDCs) exhibit a rich variety of emissive states and intriguing valley spin-selection rules, the effective modulation of which is crucial for excitonic physics and related device applications. Strain or high pressure provides the possibility to tune the energy of the interlayer excitons; however, the reported emission intensity is substantially quenched, which greatly limits their practical application in optoelectronic devices. Here, via applying uniaxial strain based on polyvinyl alcohol (PVA) encapsulation technique, we report enhanced layer-engineered interlayer exciton emission intensity with largely modulated emission energy in WSe2/WS2 heterobilayer and heterotrilayer. Both momentum-direct and momentum-indirect interlayer excitons were observed, and their emission energies show an opposite shift tendency upon applied strain, which agrees with our DFT calculations. We further demonstrate that intralayer and interlayer exciton states with low phonon interactions can be modulated through the mechanical strain applied to the PVA substrate at low temperatures. Due to strain-induced breaking of the 3-fold rotational symmetry, we observe the enhanced valley polarization of interlayer excitons. Our study contributes to the understanding and modulation of the optical properties of interlayer excitons, which could be exploited for optoelectronic device applications.

Hierarchical processing enabled by 2D ferroelectric semiconductor transistor for low-power and high-efficiency AI vision system.

The growth of data and Internet of Things challenges traditional hardware, which encounters efficiency and power issues owing to separate functional units for sensors, memory, and computation. In this study, we designed an α-phase indium selenide (α-In2Se3) transistor, which is a two-dimensional ferroelectric semiconductor as the channel material, to create artificial optic-neural and electro-neural synapses, enabling cutting-edge processing-in-sensor (PIS) and computing-in-memory (CIM) functionalities. As an optic-neural synapse for low-level sensory processing, the α-In2Se3 transistor exhibits a high photoresponsivity (2855 A/W) and detectivity (2.91 × 1014 Jones), facilitating efficient feature extraction. For high-level processing tasks as an electro-neural synapse, it offers a fast program/erase speed of 40 ns/50 µs and ultralow energy consumption of 0.37 aJ/spike. An AI vision system using α-In2Se3 transistors has been demonstrated. It achieved an impressive recognition accuracy of 92.63% within 12 epochs owing to the synergistic combination of the PIS and CIM functionalities. This study demonstrates the potential of the α-In2Se3 transistor in future vision hardware, enhancing processing, power efficiency, and AI applications.

Defect Engineering of 2D Semiconductors for Dual Control of Emission and Carrier Polarity.

2D transition metal dichalcogenides (TMDCs) are considered as promising materials in post-Moore technology. However, the low photoluminescence quantum yields (PLQY) and single carrier polarity due to the inevitable defects during material preparation are great obstacles to their practical applications. Here, an extraordinary defect engineering strategy is reported based on first-principles calculations and realize it experimentally on WS2 monolayers by doping with IIIA atoms. The doped samples with large sizes possess both giant PLQY enhancement and effective carrier polarity modulation. Surprisingly, the high PL emission maintained even after one year under ambient environment. Moreover, the constructed p-n homojunctions shows high rectification ratio (≈2200), ultrafast response times and excellent stability. Meanwhile, the doping strategy is universally applicable to other TMDCs and dopants. This smart defect engineering strategy not only provides a general scheme to eliminate the negative influence of defects, but also utilize them to achieve desired optoelectronic properties for multifunctional applications.

Reconfigurable Artificial Synapse Based on Ambipolar Floating Gate Memory.

Artificial synapse networks capable of massively parallel computing and mimicking biological neural networks can potentially improve the processing efficiency of existing information technologies. Semiconductor devices functioning as excitatory and inhibitory synapses are crucial for developing intelligence systems, such as traffic control systems. However, achieving reconfigurability between two working modes (inhibitory and excitatory) and bilingual synaptic behavior in a single transistor remains challenging. This study successfully mimics a bilingual synaptic response using an artificial synapse based on an ambipolar floating gate memory comprising tungsten selenide (WSe2)/hexagonal boron nitride (h-BN)/ molybdenum telluride (MoTe2). In this WSe2/h-BN/MoTe2 structure, ambipolar semiconductors WSe2 and MoTe2 are inserted as channel and floating gates, respectively, and h-BN serves as the tunneling barrier layer. Using either positive or negative pulse amplitude modulations at the control gate, this device with bipolar channel conduction produced eight distinct resistance states. Based on this, we experimentally projected that we could achieve 490 memory states (210 hole-resistance states + 280 electron-resistance states). Using the bipolar charge transport and multistorage states of WSe2/h-BN/MoTe2 floating gate memory, we mimicked reconfigurable excitatory and inhibitory synaptic plasticity in a single device. Furthermore, the convolution neural network formed by these synaptic devices can recognize handwritten digits with an accuracy of >92%. This study identifies the unique properties of heterostructure devices based on two-dimensional materials as well as predicts their applicability in advanced recognition of neuromorphic computing.

Multifunctional Optoelectronic Synapses Based on Arrayed MoS2 Monolayers Emulating Human Association Memory.

Optoelectronic synaptic devices integrating light-perception and signal-storage functions hold great potential in neuromorphic computing for visual information processing, as well as complex brain-like learning, memorizing, and reasoning. Herein, the successful growth of MoS2 monolayer arrays assisted by gold nanorods guided precursor nucleation is demonstrated. Optical, spectral, and morphology characterizations of MoS2 prove that arrayed flakes are homogeneous monolayers, and they are further fabricated as optoelectronic devices showing featured photocurrent loops and stable optical responses. Typical synaptic behaviors of photo-induced short-term potentiation, long-term potentiation, and paired pulse facilitation are recorded under different light stimulations of 450, 532, and 633 nm lasers at various excitation powers. A visual sensing system consisting of 5 × 6 pixels is constructed to simulate the light-sensing image mapped by forgetting curves in real time. Moreover, the system presents the ability of utilizing associated images to restore vague and incomplete memories, which successfully mimics human intelligent behaviors of association memory and logical reasoning. The work emulates the brain-like artificial intelligence using arrayed 2D semiconductors, which paves an avenue to achieve smart retina and complex brain-like system.