Oxygen Vacancies Controlled Highly Stable Bilayer Analog Synapse Used for Neuromorphic Computing Systems Oxygen Vacancies Controlled Highly Stable Bilayer Analog Synapse Used for Neuromorphic Computing Systems AITranslate

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DOI：https://doi.org/10.1021/acsaelm.2c00272

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This study investigated an oxygen-vacancy-controlled bilayer TiN/TaOy/TaOx/Pt memristive synaptic device for neuromorphic computing. Multilevel characteristics of the synaptic device were observed with RESET voltage varying between −1.4 and −1.9 V. The device shows highly stable reparative 200 potentiation and depression cycles. The high nonlinearity results of αp = 0.83 for potentiation and αd = −2.03 for depression were observed with the device’s potentiation and depression functions. The device also exhibits a highly stable DC endurance of at least 1000 cycles, an AC pulse endurance of 1 M, and a steady retention of 104 s at 100 °C without any degradation. Furthermore, a Hopfield neural network (HNN) is trained to recognize a 28 × 28 pixels image as an input, representing 784 synapses. In 23 epochs, the HNN successfully identified the input image with training accuracy over 92%. This bilayer memristive device can be highly suitable for neuromorphic devices in the development of neuromorphic-computing field.