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Organic heterojunction synaptic device with ultra high recognition rate for neuromorphic computing
Nano Research
Published: 14 March 2024
Downloads:15

Traditional computing structures are blocked by the von Neumann bottleneck, and neuromorphic computing devices inspired by the human brain which integrate storage and computation have received more and more attention. Here, a flexible organic device with 2,7-dioctyl[1] benzothieno [3,2-b][1] benzothiophene (C8-BTBT) and 2,9-didecyldinaphtho [2,3-b:2′,3′-f] thieno [3,2-b] thiophene (C10-DNTT) heterostructural channel having excellent synaptic behaviors was fabricated on muscovite (MICA) substrate, which has a memory window greater than 20 V. This device shows better electrical characteristics than organic field effect transistors with single organic semiconductor channel. Furthermore, the device simulates organism synaptic behaviors successfully, such as paired-pulse facilitation (PPF), long-term potentiation/depression (LTP/LTD) process, and transition from short-term memory (STM) to long-term memory (LTM) by optical and electrical modulations. Importantly, the neuromorphic computing function was verified using the Modified National Institute of Standards and Technology (MNIST) pattern recognition, with a recognition rate nearly 100% without noise. This research proposes a flexible organic heterojunction with the ultra-high recognition rate in MNIST pattern recognition and provides the possibility for future flexible wearable neuromorphic computing devices.

Research Article Issue
Analog ferroelectric domain-wall memories and synaptic devices integrated with Si substrates
Nano Research 2022, 15 (4): 3606-3613
Published: 10 December 2021
Downloads:32

Brain-inspired neuromorphic computing can overcome the energy and throughput limitations of traditional von Neumann-type computing systems, which requires analog updates of their artificial synaptic strengths for the best recognition performance and low energy consumption. Here, we report synaptic devices made from highly insulating ferroelectric LiNbO3 (LNO) thin films bonded to SiO2/Si wafers. Through the creation/annihilation of periodically arrayed antiparallel domains within LNO nanocells, which are stimulated using positive/negative voltage pulses (synaptic plasticity), we can modulate the synaptic conductance linearly by controlling the number of the conducting domain walls. The multilevel conductance is nonvolatile and reproducible with negligible dispersion over 100 switching cycles, representing much better performance than that of random defect-based nonlinear memristors, which generally exhibit large-scale resistance dispersion. The simulation of a neuromorphic network using these LNO artificial synapses achieves 95.6% recognition accuracy for faces, thus approaching the theoretical yield of ideal neuromorphic computing devices.

Research Article Issue
A high-speed 2D optoelectronic in-memory computing device with 6-bit storage and pattern recognition capabilities
Nano Research 2022, 15 (3): 2472-2478
Published: 11 August 2021
Downloads:36

The explosively developed era of big-data compels the increasing demand of nonvolatile memory with high efficiency and excellent storage properties. Herein, we fabricated a high-speed photoelectric multilevel memory device for neuromorphic computing. The novel two-dimensional (2D) MoSSe with a unique Janus structure was employed as the channel, and the stack of Al2O3/black phosphorus quantum dots (BPQDs)/Al2O3 was adopted as the dielectric. The storage performance of the resulting memory could be verified by the endurance and retention tests, in which the device could remain stable states of programming and erasing even after 1, 000 cycles and 1, 000 s. The multibit storage could be realized through both different voltage amplitudes and pulse numbers, which could achieve 6 bits (64 distinguishable levels) under pulse width of 50 ns. Furthermore, our memory device also could realize the simulations of synapses in human brain with optical and electric modulations synergistically, such as excitatory post-synaptic current (EPSC), long-term potentiation/depression (LTP/LTD), and spike-timing-dependent plasticity (STDP). Neuromorphic computing was successfully achieved through a high recognition of handwritten digits up to 92.5% after 103 epochs. This research is a promising avenue for the future development of efficient memory and artificial neural network systems.

Research Article Issue
Atomic layer deposited 2D MoS2 atomic crystals: From material to circuit
Nano Research 2020, 13 (6): 1644-1650
Published: 23 April 2020
Downloads:41

Atomic layer deposition (ALD) can be used for wafer-scale synthesis of 2D materials. In this paper, a novel, reliable, secure, low-cost, and high-efficiency process for the fabrication of MoS2 is introduced and investigated. The resulting 2D materials show high carrier-mobility as well as excellent electrical uniformity. Using molybdenum pentachloride (MoCl5) and hexamethyldisilathiane (HMDST) as ALD precursors, thickness-controlled MoS2 films are uniformly deposited on a 50 mm sapphire and a 100 mm silica substrate. This is done with a high growth-rate (up to 0.90 Å/cycle). Large-scale top-gated FET arrays are fabricated using the films, with a room-temperature mobility of 0.56 cm2/(V·s) and a high on/off current ratio of 106. Excellent electrical uniformity is observed in the whole sapphire wafer. Additionally, logical circuits, including inverters, NAND, AND, NOR, and OR gates, are realized successfully with a high-k HfO2 dielectric layer. Our inverters exhibit a fast response frequency of 50 Hz and a DC-voltage gain of 4 at VDD = 4 V. These results indicate that the new method has the potential to synthesize high quality MoS2 films on a large-scale, with hypo-toxicity and enhanced efficiency, which can facilitate a broader range of applications in the future.

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