4.6 Article

Superlow Power Consumption Artificial Synapses Based on WSe2 Quantum Dots Memristor for Neuromorphic Computing

期刊

RESEARCH
卷 2022, 期 -, 页码 -

出版社

AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.34133/2022/9754876

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资金

  1. National Natural Science Foundation of China [62104058, 61874158, 62004056]
  2. Natural Science Foundation of Hebei Province [F2021201009]
  3. Science and Technology Project of Hebei Education Department [QN2021026]
  4. Advanced Talents Incubation Program of the Hebei University [521000981426]
  5. National Key R&D Plan Nano Frontier Key Special Project [2021YFA1200502]
  6. Cultivation Projects of National Major RD Project [92164109]
  7. Special Project of Strategic Leading Science and Technology of Chinese Academy of Sciences [XDB44000000-7]
  8. Hebei Basic Research Special Key Project [F2021201045]
  9. Support Program for the Top Young Talents of Hebei Province [70280011807]
  10. Supporting Plan for 100 Excellent Innovative Talents in Colleges and Universities of Hebei Province [SLRC2019018]
  11. Outstanding Young Scientific Research and Innovation Team of Hebei University [605020521001]
  12. Special Support Funds for National High Level Talents [041500120001]
  13. High-level Talent Research Startup Project of Hebei University [521000981426]

向作者/读者索取更多资源

A high-performance, superlow power consumption memristor device based on WSe2 quantum dots is demonstrated, showing excellent resistive switching memory behavior and good stability. The device is able to simulate different functions of artificial synapses and has high accuracy in digit recognition, paving the way for future low power neuromorphic computing.
As the emerging member of zero-dimension transition metal dichalcogenide, WSe2 quantum dots (QDs) have been applied to memristors and exhibited better resistance switching characteristics and miniaturization size. However, low power consumption and high reliability are still challenges for WSe2 QDs-based memristors as synaptic devices. Here, we demonstrate a high-performance, superlow power consumption memristor device with the structure of Ag/WSe2 QDs/La0.3Sr0.7MnO3/SrTiO3. The device displays excellent resistive switching memory behavior with a R-OFF/R-ON ratio of similar to 5 x 10(3), power consumption per switching as low as 0.16 nW, very low set, and reset voltage of similar to 0.52 V and similar to -0.19 V with excellent cycling stability, good reproducibility, and decent data retention capability. The superlow power consumption characteristic of the device is further proved by the method of density functional theory calculation. In addition, the influence of pulse amplitude, duration, and interval was studied to gradually modulating the conductance of the device. The memristor has also been demonstrated to simulate different functions of artificial synapses, such as excitatory postsynaptic current, spike timing-dependent plasticity, long-term potentiation, long-term depression, and paired-pulse facilitation. Importantly, digit recognition ability based on the WSe2 QDs device is evaluated through a three-layer artificial neural network, and the digit recognition accuracy after 40 times of training can reach up to 94.05%. This study paves a new way for the development of memristor devices with advanced significance for future low power neuromorphic computing.

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