Synaptic behaviors of a single metal-oxide-metal resistive device

Sang Jun Choi, Guk Bae Kim, Kyoobin Lee, Ki Hong Kim, Woo Young Yang, Soohaeng Cho, Hyung Jin Bae, Dong Seok Seo, Sang Il Kim, Kyoung Jin Lee

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1019-1025
Number of pages7
JournalApplied Physics A: Materials Science and Processing
Issue number4
Publication statusPublished - 2011 Mar

ASJC Scopus subject areas

  • General Chemistry
  • General Materials Science


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