반도체 나노소자 연구실

The current computing systems have been successfully developed with a von Neumann architecture that is comprised of separated processing units and memory units. However, as the task for processing becomes more complex and the amount of data becomes more massive, they suffer from the high power consumption and delayed data transfer called von Neumann bottleneck. In order to overcome this limitation, the neuromorphic systems as a novel computing paradigm have been actively investigated. The neuromorphic systems emulate the human brain in computing, learning, training, and memory operation with high energy efficiency and low power. The artificial synapses emulating biological synaptic behavior as essential elements in neuromorphic systems have been under active investigation. They are connected to neuron circuits with a high connectivity and allow for simultaneous parallel signal processing and consequently adaptive learning/memory functions with a modulated synaptic weight. Our laboratory investigates the artificial synapses to develop neuromorphic systems realizing the brain operations. Among the various synaptic devices developed for use in artificial synapses to date, memristors with two-terminal metal/resistive-switching-layer/metal structures, which are analogous to presynaptic-neuron/synapse/postsynaptic-neuron in biological systems, have been mostly actively investigated. Analog resistive switching memristors are particularly desirable to emulate the analog tuning of synaptic weight in biological synapses. Besides analog behavior, linearity of the resistance change, representing the identical incremental tuning of synaptic weight with the repetition of input pulses, is requisite for fast learning with simple neuron circuit operation by determining the synaptic weig