Highly reliable resistive switching based on Li intercalation in Cu/(Co-Fe-B)x(SiO2-z)100-x/LiNbO2-y/Cu nanocomposite memristors for neuromorphic computing

This study investigates the properties and applications of Cu/(Co-Fe-B) x (SiO z ) 100-x /LiNbO y /Cu (y ~ z ~ 1.4) nanocomposite (NC) memristors, with active layers acquired via ion-beam sputtering of stoichiometric LiNbO 3 and compound Co 40 Fe 40 B 20 -SiO 2 targets on the sitall substrate without external heating during the synthesis process. The devices exhibit quasi-analog resistive switching (RS), alongside high endurance exceeding 10000 cycles between 2 states and 25000 cycles between 50 states, which are advantageous for neuromorphic computing systems. RS behavior is established to be attributed to the Li+ intercalation and the forming of the self-organized Cu clusters inside the LiNbO 1.4 layer, as well as the presence of the Li-accumulating membrane (SiO 1.1 layer) between NC and LiNbO 1.4 . These factors explain both the multi-level (analogous) nature of the resistive states and their long retention time. To assess their practical viability, a two-layer memristive network was simulated for classification of handwritten digits from the MNIST dataset. Results revealed that the conductance state range significantly influences network performance, with broader ranges yielding superior accuracy. The conductance state variations also substantially diminish classification performance, emphasizing the necessity for meticulous selection of training parameters. The study concludes that employing long-term depression and long-term potentiation curves with the same pulses (with handpicked parameters) is preferable for energy-efficient neuromorphic computing, as opposed to a write-verify algorithm or long-lasting fine-tuning of states, which, while effective, incur higher complexity, as well as time and energy costs.