A carbon nanotube (CNT) synapse emulates a biological synapse with its dynamic logic, learning, and memory functions induced by the interactions between CNTs and hydrogen ions in an electrochemical cell. A circuit of CNT synapses operates with extremely low-energy consumption and could potentially emulate the functions of the neuronal network.

We report an analog neuromorphic module composed of p-type carbon nanotube (CNT) synapses and an integrate-and-fire (I&F) circuit. The CNT synapse has a field-effect transistor structure with a random CNT network as its channel and an aluminum oxide dielectric layer implanted with indium ions as its gate. A positive voltage pulse (spike) applied on the gate attracts electrons into the defect sites of the gate dielectric layer, and the trapped electrons are gradually released after the pulse is removed. The electrons modify the hole concentration and induce a dynamic postsynaptic current in the CNT channel. Multiple input spikes induce excitatory or inhibitory postsynaptic currents via excitatory or inhibitory CNT synapses, which flow toward an I&F circuit to trigger output spikes. The dynamic transfer function between the input and output spikes of the neuromorphic module is analyzed. The module could potentially be scaled up to emulate biological neural networks and their functions.

A nonvolatile analog memory transistor is demonstrated by integrating C60 molecules as charge storage molecules in the transistor gate, and carbon nanotubes (CNTs) in the transistor channel. The currents through the CNT channel can be tuned quantitatively and reversibly to analog values by controlling the number of electrons trapped in the C60 molecules. After tuning, the electrons trapped in the C60 molecules in the gate, and the current through the CNT channel, can be preserved in a nonvolatile manner, indicating the characteristics of the nonvolatile analog memory.

A carbon nanotube (CNT) electronic synapse which emulates a biological synapse with its dynamic logic, learning, and memory functions is induced by the interactions between CNTs and hydrogen ions in an electrochemical cell. Temporally correlated spikes can trigger the dynamic interactions between CNTs and hydrogen ions, resulting in spike‐timing dependent plasticity (STDP) for memory and learning, as reported by Yong Chen and co‐workers .

It is extremely challenging to imitate neural networks with their high-speed parallel signal processing, low power consumption, and intelligent learning capability. In this work, we report a spike neuromorphic module composed of ''synapstors'' made from carbon nanotube/C60/polyimide composite and ''CMOS Somas'' made from complementary metal-oxide semiconductor electronic circuits. The ''synapstor'' emulates a biological synapse with spike signal processing, plasticity, and memory; the ''CMOS Soma'' emulates a Soma in a biological neuron with analog parallel signal processing and spike generation. Spikes, short potential pulses, and input to the synapstors trigger postsynaptic currents and generate output spikes from the CMOS Somas in a parallel manner with low power consumption. The module can be modified dynamically on the basis of the synapstor plasticity. Spike neuromorphic modules could potentially be scaled up to emulate biologic neural networks and their functions.