Due to the increasing importance of artificial intelligence (AI), significant recent effort has been devoted to the development of neuromorphic circuits that seek to emulate the energy‐efficient information processing of the brain. While non‐volatile memory (NVM) based on resistive switches, phase‐change memory, and magnetic tunnel junctions has shown potential for implementing neural networks, additional multi‐terminal device concepts are required for more sophisticated bio‐realistic functions. Of particular interest are memtransistors based on low‐dimensional nanomaterials, which are capable of electrostatically tuning memory and learning behavior at the device level. Herein, a conceptual overview of the memtransistor is provided in the context of neuromorphic circuits. Recent progress is surveyed for memtransistors and related multi‐terminal NVM devices including dual‐gated floating‐gate memories, dual‐gated ferroelectric transistors, and dual‐gated van der Waals heterojunctions. The different materials systems and device architectures are classified based on the degree of control and relative tunability of synaptic behavior, with an emphasis on device concepts that harness the reduced dimensionality, weak electrostatic screening, and phase‐changes properties of nanomaterials. Finally, strategies for achieving wafer‐scale integration of memtransistors and multi‐terminal NVM devices are delineated, with specific attention given to the materials challenges for practical neuromorphic circuits.
Spiking neural networks exploit spatiotemporal processing, spiking sparsity, and high interneuron bandwidth to maximize the energy efficiency of neuromorphic computing. While conventional silicon-based technology can be used in this context, the resulting neuron-synapse circuits require multiple transistors and complicated layouts that limit integration density. Here, we demonstrate unprecedented electrostatic control of dual-gated Gaussian heterojunction transistors for simplified spiking neuron implementation. These devices employ wafer-scale mixed-dimensional van der Waals heterojunctions consisting of chemical vapor deposited monolayer molybdenum disulfide and solution-processed semiconducting single-walled carbon nanotubes to emulate the spike-generating ion channels in biological neurons. Circuits based on these dual-gated Gaussian devices enable a variety of biological spiking responses including phasic spiking, delayed spiking, and tonic bursting. In addition to neuromorphic computing, the tunable Gaussian response has significant implications for a range of other applications including telecommunications, computer vision, and natural language processing.
more » « less- NSF-PAR ID:
- 10153880
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Memristive systems offer biomimetic functions that are being actively explored for energy‐efficient neuromorphic circuits. In addition to providing ultimate geometric scaling limits, 2D semiconductors enable unique gate‐tunable responses including the recent realization of hybrid memristor and transistor devices known as memtransistors. In particular, monolayer MoS2memtransistors exhibit nonvolatile memristive switching where the resistance of each state is modulated by a gate terminal. Here, further control over the memtransistor neuromorphic response through the introduction of a second gate terminal is gained. The resulting dual‐gated memtransistors allow tunability over the learning rate for non‐Hebbian training where the long‐term potentiation and depression synaptic behavior is dictated by gate biases during the reading and writing processes. Furthermore, the electrostatic control provided by dual gates provides a compact solution to the sneak current problem in traditional memristor crossbar arrays. In this manner, dual gating facilitates the full utilization and integration of memtransistor functionality in highly scaled crossbar circuits. Furthermore, the tunability of long‐term potentiation yields improved linearity and symmetry of weight update rules that are utilized in simulated artificial neural networks to achieve a 94% recognition rate of hand‐written digits.
