Abstract
Next-generation synaptic devices with multiple non-volatile states, high endurance and high-temperature operation are highly desired in the era of big data. Here, high-performance memristors are fabricated using La: HfO2(HLO)/La2/3Sr1/3MnO3(LSMO) heterostructures on Si substrate, with domain matching epitaxial structure using SrTiO3(STO) as buffer layer. The devices possess high reliability, nonvolatility, low fluctuation rate (<2.5 %) and the highest number of states per cell (32 states or 5 bits) among the reported Hf-based ferroelectric memories at room temperature (25 °C) and high temperature (85 °C). Moreover, the device exhibits high endurance of 109 cycles and excellent uniformity at the room and high temperatures. The functionality of long-term plasticity in the synaptic device is obtained with high precision (128 states), reproducibility (cycle-to-cycle variation, ∼4.7 %) and linearity. Then, we simulate one system using the stable performance at high temperature that detects the speed of moving targets, which achieves high accuracy of 98 % and 99 % on Human Motion and MNIST datasets, respectively. Furthermore, we have built a hardware circuit to realize a spiking neural network (SNN) system for digital pattern online learning, which demonstrates the capability of the device in brain-like computing applications.
Original language | English (US) |
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Pages (from-to) | 365-373 |
Number of pages | 9 |
Journal | Materials Today |
Volume | 80 |
DOIs | |
State | Published - Nov 2024 |
Keywords
- Domain matching epitaxy
- Memristors
- Spiking neural network
ASJC Scopus subject areas
- General Materials Science
- Condensed Matter Physics
- Mechanics of Materials
- Mechanical Engineering