• JSTS:Journal of Semiconductor Technology and Science
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• 제15권6호
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• pp.658-663
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• 2015

In the previous work, we have proposed an integrate-and-fire neuron circuit and synaptic device based on the floating body MOSFET [1-3]. Integrate-and-Fire(I&F) neuron circuit emulates the biological neuron characteristics such as integration, threshold triggering, output generation, refractory period using floating body MOSFET. The synaptic device has short-term and long-term memory in a single silicon device. In this paper, we connect the neuron circuit and the synaptic device using current mirror circuit for summation of post synaptic pulses. We emulate spike-timing-dependent-plasticity (STDP) characteristics of the synapse using feedback voltage without controller or clock. Using memory device in the logic circuit, we can emulate biological synapse and neuron with a small number of devices.

• JSTS:Journal of Semiconductor Technology and Science
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• 제14권6호
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• pp.755-759
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• 2014

We propose an integrate-and-fire neuron circuit and synaptic devices with the floating body MOSFETs. The synaptic devices consist of a floating body MOSFET to imitate biological synaptic characteristics. The synaptic learning is performed by hole accumulation. The synaptic device has short-term and long-term memory in a single silicon device. I&F neuron circuit emulate the biological neuron characteristics such as integration, threshold triggering, output generation, and refractory period, using floating body MOSFET. The neuron circuit sends feedback signal to the synaptic transistor for long-term memory.

• JSTS:Journal of Semiconductor Technology and Science
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• 제17권2호
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• pp.174-179
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• 2017

We designed the CMOS analog integrate and fire (I&F) neuron circuit for driving memristor based on resistive-switching random access memory (RRAM). And we fabricated the RRAM device that have $HfO_2$ switching layer using atomic layer deposition (ALD). The RRAM device has gradual set and reset characteristics. By spice modeling of the synaptic device, we performed circuit simulation of synaptic device and CMOS neuron circuit. The neuron circuit consists of a current mirror for spatial integration, a capacitor for temporal integration, two inverters for pulse generation, a refractory part, and finally a feedback part for learning of the RRAM. We emulated the spike-timing-dependent-plasticity (STDP) characteristic that is performed automatically by pre-synaptic pulse and feedback signal of the neuron circuit. By STDP characteristics, the synaptic weight, conductance of the RRAM, is changed without additional control circuit.

• BioChip Journal
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• 제12권4호
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• pp.332-339
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• 2018

The future neuro-prosthetic devices would be required spikes data monitoring through sub-nanoscale transistors that enables to neuroscientists and clinicals for scalable, wireless and implantable applications. This research investigates the spikes monitoring through integrated CNT front-end amplifier for neuro-prosthetic diagnosis. The proposed carbon nanotube-based architecture consists of front-end amplifier (FEA), integrate fire neuron and pseudo resistor technique that observed high electrical performance through neural activity. A pseudo resistor technique ensures large input impedance for integrated FEA by compensating the input leakage current. While carbon nanotube based FEA provides low-voltage operation with directly impacts on the power consumption and also give detector size that demonstrates fidelity of the neural signals. The observed neural activity shows amplitude of spiking in terms of action potential up to $80{\mu}V$ while local field potentials up to 40 mV by using proposed architecture. This fully integrated architecture is implemented in Analog cadence virtuoso using design kit of CNT process. The fabricated chip consumes less power consumption of $2{\mu}W$ under the supply voltage of 0.7 V. The experimental and simulated results of the integrated FEA achieves $60G{\Omega}$ of input impedance and input referred noise of $8.5nv/{\sqrt{Hz}}$ over the wide bandwidth. Moreover, measured gain of the amplifier achieves 75 dB midband from range of 1 KHz to 35 KHz. The proposed research provides refreshing neural recording data through nanotube integrated circuit and which could be beneficial for the next generation neuroscientists.