| Autor: |
Jena AK; Laboratory for Low-dimensional Materials, Institute of Physics, Bhubaneswar 751005, India.; Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai 400094, India., Sahu MC; Laboratory for Low-dimensional Materials, Institute of Physics, Bhubaneswar 751005, India.; Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai 400094, India., Mohanan KU; Department of Electronic Engineering, Gachon University, Seongnam 13120, Republic of Korea., Mallik SK; Laboratory for Low-dimensional Materials, Institute of Physics, Bhubaneswar 751005, India.; Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai 400094, India., Sahoo S; Laboratory for Low-dimensional Materials, Institute of Physics, Bhubaneswar 751005, India.; Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai 400094, India., Pradhan GK; Department of Physics, School of Applied Sciences, KIIT Deemed to be University, Bhubaneswar, Odisha 751024, India., Sahoo S; Laboratory for Low-dimensional Materials, Institute of Physics, Bhubaneswar 751005, India.; Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai 400094, India. |
| Abstrakt: |
Memristive devices are among the most emerging electronic elements to realize artificial synapses for neuromorphic computing (NC) applications and have potential to replace the traditional von-Neumann computing architecture in recent times. In this work, pulsed laser deposition-manufactured Ag/TiO 2 /Pt memristor devices exhibiting digital and analog switching behavior are considered for NC. The TiO 2 memristor shows excellent performance of digital resistive switching with a memory window of order ∼10 3 . Furthermore, the analog resistive switching offers multiple conductance levels supporting the development of the bioinspired synapse. A possible mechanism for digital and analog switching behavior in our device is proposed. Remarkably, essential synaptic functions such as pair-pulse facilitation, long-term potentiation (LTP), and long-term depression (LTD) are successfully realized based on the change in conductance through analog memory characteristics. Based on the LTP-LTD, a neural network simulation for the pattern recognition task using the MNIST data set is investigated, which shows a high recognition accuracy of 95.98%. Furthermore, more complex synaptic behavior such as spike-time-dependent plasticity and Pavlovian classical conditioning is successfully emulated for associative learning of the biological brain. This work enriches the TiO 2 -based resistive random-access memory, which provides information about the simultaneous existence of digital and analog behavior, thereby facilitating the further implementation of memristors in low-power NC. |
