Choi, Sang-Jun Kim, Guk-Bae Lee, Kyoobin Kim, Ki-Hong Yang, Woo-Young Cho, Soohaeng Bae, Hyung-Jin Seo, Dong-Seok Kim, Sang-Il Lee, Kyung-Jin 2024-01-20T17:32:34Z 2024-01-20T17:32:34Z 2021-09-02 2011-03 0947-8396 https://pubs.kist.re.kr/handle/201004/130600 The mammalian brain is far superior to today's electronic circuits in intelligence and efficiency. Its functions are realized by the network of neurons connected via synapses. Much effort has been extended in finding satisfactory electronic neural networks that act like brains, i.e., especially the electronic version of synapse that is capable of the weight control and is independent of the external data storage. We demonstrate experimentally that a single metal-oxide-metal structure successfully stores the biological synaptic weight variations (synaptic plasticity) without any external storage node or circuit. Our device also demonstrates the reliability of plasticity experimentally with the model considering the time dependence of spikes. All these properties are embodied by the change of resistance level corresponding to the history of injected voltage-pulse signals. Moreover, we prove the capability of second-order learning of the multi-resistive device by applying it to the circuit composed of transistors. We anticipate our demonstration will invigorate the study of electronic neural networks using non-volatile multi-resistive device, which is simpler and superior compared to other storage devices. English SPRINGER HEIDELBERG DEPENDENCE RESISTANCE Synaptic behaviors of a single metal-oxide-metal resistive device Article 10.1007/s00339-011-6282-7 1 APPLIED PHYSICS A-MATERIALS SCIENCE & PROCESSING, v.102, no.4, pp.1019 - 1025 APPLIED PHYSICS A-MATERIALS SCIENCE & PROCESSING 102 4 1019 1025 scie scopus 000288253600034 2-s2.0-79959343819 Materials Science, Multidisciplinary Physics, Applied Materials Science Physics Article DEPENDENCE RESISTANCE