Name
Title | ||
|---|---|---|
Long-term memory performance with learning behavior of artificial synaptic memristor based on stacked solution-processed switching layers |
Abstract | ||
|---|---|---|
In this work, oxide resistive random access memory (OxRRAM) devices with stacked solution-processed (SP) metal oxide (MO) layers were fabricated to investigate artificial synaptic behavior such as long-term potentiation (LTP) and long-term depression (LTD). The stacked RRAM devices exhibited stable and repeated bipolar IV curves with operation voltage lower than the similar to 0.5 V and a switching ratio larger than 2x10(4). Also, with the stimuli from external consecutive pulses, the stacked devices demonstrated learning-forgetting-relearning behavior similar to neuron-induced behavior in the human brain. Finally, based on stable long-term memory performance, the pattern recognition system with an artificial neuron network (ANN) algorithm was simulated with the recognition accuracy higher than 95%. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISCAS51556.2021.9401493 | 2021 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) |
Keywords | DocType | ISSN |
solution-processed, synaptic behavior, human brain, RRAM | Conference | 0271-4302 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zongjie Shen | 1 | 0 | 0.34 |
| Ce Zhou Zhao | 2 | 0 | 1.01 |
| Ka Lok Man | 3 | 88 | 27.99 |
| Yina Liu | 4 | 0 | 2.03 |
| Cezhou Zhao | 5 | 0 | 0.34 |