TY - JOUR
T1 - Exploring the potential of TiO2/ZrO2 memristors for neuromorphic computing
T2 - Annealing strategy and synaptic characteristics
AU - Ali, Sarfraz
AU - Hussain, Muhammad
AU - Ismail, Muhammad
AU - Iqbal, Muhammad Waqas
AU - Kim, Sungjun
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/8/30
Y1 - 2024/8/30
N2 - Artificial Neural Networks (ANNs) have reshaped computing paradigms, transcending traditional methods. Leveraging oxide-based bilayer RRAM memristors, specifically TiO2/ZrO2 deposited via sputtering, offers remarkable potential for RS memory and neuromorphic computing. This study pioneers an extensive annealing approach to counteract variability challenges in LRS and HRS during endurance tests. The Pt/TiO2/ZrO2/Pt memristor device's structural aspects are validated through cross-sectional high resolution transmission electron microscopy (HRTEM) analysis. Systematic XPS examination investigates the impact of annealing on oxygen vacancies. Successful bipolar resistive switching is unveiled through I-V characteristics, with 550°C annealing optimizing stable endurance cycling (1000 dc cycles). Conduction mechanisms during set/reset are illuminated, corroborated by Schottky emission fitting. Synaptic behavior emulation, Spike-Timing-Dependent Plasticity (STDP), and theoretical simulations with a 28×28 MNIST dataset underscore the ANN's 84.6% average recognition rate. The amalgamation of MNIST-based artificial learning and the innovative annealing strategy holds exciting potential for memory applications and advanced neuromorphic explorations.
AB - Artificial Neural Networks (ANNs) have reshaped computing paradigms, transcending traditional methods. Leveraging oxide-based bilayer RRAM memristors, specifically TiO2/ZrO2 deposited via sputtering, offers remarkable potential for RS memory and neuromorphic computing. This study pioneers an extensive annealing approach to counteract variability challenges in LRS and HRS during endurance tests. The Pt/TiO2/ZrO2/Pt memristor device's structural aspects are validated through cross-sectional high resolution transmission electron microscopy (HRTEM) analysis. Systematic XPS examination investigates the impact of annealing on oxygen vacancies. Successful bipolar resistive switching is unveiled through I-V characteristics, with 550°C annealing optimizing stable endurance cycling (1000 dc cycles). Conduction mechanisms during set/reset are illuminated, corroborated by Schottky emission fitting. Synaptic behavior emulation, Spike-Timing-Dependent Plasticity (STDP), and theoretical simulations with a 28×28 MNIST dataset underscore the ANN's 84.6% average recognition rate. The amalgamation of MNIST-based artificial learning and the innovative annealing strategy holds exciting potential for memory applications and advanced neuromorphic explorations.
KW - Annealing
KW - Artificial learning
KW - Biotic functions
KW - Bipolar RS switching
KW - Filamentary conduction
KW - Neural network
KW - RRAM memristor
UR - http://www.scopus.com/inward/record.url?scp=85194071066&partnerID=8YFLogxK
U2 - 10.1016/j.jallcom.2024.174802
DO - 10.1016/j.jallcom.2024.174802
M3 - Article
AN - SCOPUS:85194071066
SN - 0925-8388
VL - 997
JO - Journal of Alloys and Compounds
JF - Journal of Alloys and Compounds
M1 - 174802
ER -