A MoS₂-based coplanar neuron transistor for logic applications

Abstract

The human brain is an extremely complex system of 10-10 neurons. To construct brain-like neuromorphic hardware, the neuron unit should be implemented effectively. Here, we report a neuron transistor based on a MoS flake, which has summation and threshold functions similar to biological neurons and may act as a basic neuron unit in neuromorphic hardware. The neuron transistor is composed of a floating gate and two control gates. A heavily doped silicon substrate serves as the floating gate, while the two control gates are capacitively coupled with the floating gate. The neuron transistor can be well controlled by the two control gates individually or simultaneously. The drain current can be modulated by the input voltages at the control gates. While the current response of the neuron transistor has a large dependence on the magnitude of the input signal, it shows little dependence on the frequency of the input signal. To demonstrate the potential neuromorphic application of the neuron transistor, functions including abacus-like function, AND logic and OR logic are realized in the neuron transistor.

Two-Dimensional Near-Atom-Thickness Materials for Emerging Neuromorphic Devices and Applications

“…Although this model presents some advantages over the IF and LIF such as neuron-like precision control of the spiking rate, its realization generally demands very complex circuits. Up-to-date developments of 2D materials-based artificial neurons can be classified into two categories: some reports demonstrated artificial neurons with 2D materials-based TSMs ( Chen et al., 2019b ; Dev et al., 2020 ; Hao et al., 2020 ; Kalita et al., 2019 ), whereas others employed 2D materials-based FETs ( Bao et al., 2019 ; Beck et al., 2020 ; Das et al., 2019 ; Hu et al., 2017 ).…”

“…FET-based approaches employing 2D materials have been explored to realize artificial neurons ( Bao et al., 2019 ; Beck et al., 2020 ; Das et al., 2019 ; Hu et al., 2017 ). Recent reports demonstrated the viability of FET-based neurons for applications in sound localization ( Das et al., 2019 ) and logic gate operation, along with single-neuron implementations.…”

“…Hu et al. reported an exfoliated MoS 2 -based coplanar neuron transistor with one floating gate and two control gates ( Hu et al., 2017 ). The ability to control the transistor with both control gates simultaneously was implemented to emulate the summation function of a biological neuron.…”

Silicon neuron transistor based on CMOS negative differential resistance (NDR)

“…In 1992, Shibata first proposed the neuron metal-oxidesemiconductor (MOS) transistor (neuMOS or vMOS) [12][13][14][15] based on a double-polysilicon CMOS process, which performed the weighted summation and threshold operations of biological neurons. In previous studies, a neuron transistor with a MoS2 flake as the channel layer was developed, and its frequency range was 0.0125-14.60 Hz [16][17][18][19][20], which is extremely close to the response frequency of biological neurons. In the present research, the 180-nm United Microelectronics Corporation (UMC) standard complementary metal-oxide-semiconductor (CMOS) process is used to implement the basic functions of neurons (weighted sum and threshold).…”

Superconducting Neuromorphic Computing Using Quantum Phase-Slip Junctions

“…Mapping conventional neural networks running on a von Neumann machine to a neuromorphic platform designed specifically for neural networks can significantly reduce power consumption and improve processing efficiency [6]. Recently, new materials and devices, including transistors [7], organic electronics [8], [9], and memristive devices [4], [10], have been developed for realization of neurons and synapses in neuromorphic chips. However, due to the CMOS technology bottleneck, the major challenge of neuromorphic systems is the massive power dissipation, which is several orders of magnitude behind a human brain [11].…”