Mimicking the Synaptic Weights and Human Forgetting Curve Using Hydrothermally Grown Nanostructured CuO Memristor Device
In the present investigation, we have fabricated copper oxide (CuO) thin film memristor by employing a hydrothermal method for neuromorphic application. The X-ray diffraction pattern confirms the films are polycrystalline in nature with the monoclinic crystal structure. The developed
devices show analog memory and synaptic property similar to biological neuron. The size dependent synaptic behavior is investigated for as-prepared and annealed CuO memristor. The results suggested that the magnitude of synaptic weights and resistive switching voltages are dependent on the
thickness of the active layer. Synaptic weights are improved in the case of the as-prepared device whereas they are inferior for annealed CuO memristor. The rectifying property similar to a biological neuron is observed only for the as-prepared device, which suggested that as-prepared devices
have better computational and learning capabilities than annealed CuO memristor. Moreover, the retention loss of the CuO memristor is in good agreement with the forgetting curve of human memory. The results suggested that hydrothermally grown CuO thin film memristor is a potential candidate
for the neuromorphic device development.
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Document Type: Research Article
Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur 416004, India
Department of Physics, Shivaji University, Kolhapur 416004, India
Department of Chemistry, Shivaji University, Kolhapur 416004, India
Sharad Institute of Technology, College of Engineering, Yadrav 416115, India
Department of Electronics, Shivaji University, Kolhapur 416004, India
Publication date: 2018-02-01
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Journal for Nanoscience and Nanotechnology (JNN) is an international and multidisciplinary peer-reviewed journal with a wide-ranging coverage, consolidating research activities in all areas of nanoscience and nanotechnology into a single and unique reference source. JNN is the first cross-disciplinary journal to publish original full research articles, rapid communications of important new scientific and technological findings, timely state-of-the-art reviews with author's photo and short biography, and current research news encompassing the fundamental and applied research in all disciplines of science, engineering and medicine.
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