Nano-Oriented Bio-Electronics Lab

Neuromorphic hardware is a sort of artificial intelligence (AI) hardware that can implement AI functions with ultra-low power by mimicking the human brain that consumes only 20 W. Implementing neuromorphic hardware requires artificial neurons and synapses that can mimic the functions of biological neural networks. We are conducting research on neuron devices and synaptic devices based on various structures (MOSFET, biristor, memristor) and materials (ferroelectric, charge trap layer, electrolyte, RRAM), and neuromorphic systems (deep neural network, spiking neural network) using them. World smallest sub-5nm all-around gate FinFET with 3nm silicon-fin width were fabricated and reported (VLSI'06). These results show feasibility to continue the Moore's law beyond the sub-5nm using the silicon-based material. World smallest sub-10nm nanowire SONOS Non-volatile memory were were demonstrated (VLSI'07). Furthermore, by combining a floating body and oxide/nitride/oxide stacked gate dielectric, the high speed DRAM operation and the non-volatile memory operation is realized in the single transistor (URAM, unified-RAM). Especially, the DRAM operation is feasible without a cell capacitor by using the floating body capacitor. According to an end-users' demand, one can allocate each operation to utilize each advantageous function. The electrical properties and thermal behaviors of CMOS devices are analyzed through simulation and modeling. Energy harvesting technology is related to scavenge electrical energy from ambient environment such as radiational, thermal and mechanical energy. Since an energy harvester can provide green and sustainable electrical energy, it becomes a strong candidate as an alternative energy source for sensors in Internet of Things (IoT). Especially, triboelect