Abstract
CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living ...computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm
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switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.
Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to those of digital systems. Though ...many devices have emerged as candidates for artificial neurons and artificial synapses, there have been few device candidates for artificial dendrites. In this work, we report on biocompatible graphene-based artificial dendrites (GrADs) that can implement dendritic processing. By using a dual side-gate configuration, current applied through a Nafion membrane can be used to control device conductance across a trilayer graphene channel, showing spatiotemporal responses of leaky recurrent, alpha, and Gaussian dendritic potentials. The devices can be variably connected to enable higher-order neuronal responses, and we show through data-driven spiking neural network simulations that spiking activity is reduced by ≤15% without accuracy loss while low-frequency operation is stabilized. This positions the GrADs as strong candidates for energy efficient bio-interfaced spiking neural networks.
Spintronic devices based on domain wall (DW) motion through ferromagnetic nanowire tracks have received great interest as components of neuromorphic information processing systems. Previous proposals ...for spintronic artificial neurons required external stimuli to perform the leaking functionality, one of the three fundamental functions of a leaky integrate-and-fire (LIF) neuron. The use of this external magnetic field or electrical current stimulus results in either a decrease in energy efficiency or an increase in fabrication complexity. In this article, we modify the shape of previously demonstrated three-terminal magnetic tunnel junction neurons to perform the leaking operation without any external stimuli. The trapezoidal structure causes a shape-based DW drift, thus intrinsically providing the leaking functionality with no hardware cost. This LIF neuron, therefore, promises to advance the development of spintronic neural network crossbar arrays.
Few-layered molybdenum disulfide (MoS2) nanosheets are poised to be at the core of low-voltage electronic device development. Upon environmental release, these two-dimensional (2D) structures can ...interact with abundant natural geocolloids. This study probes the role of dimensionality in modulating the aggregation behavior of 2D MoS2 nanosheets with plate-like geocolloids (i.e., homoionized kaolinite and montmorillonite clays). MoS2 nanosheets were exfoliated using an ethanol/water mixture, and aggregation kinetics were investigated with time-resolved dynamic light scattering at low monovalent salt concentrations and at three pH levels, in the presence and absence of Suwannee River humic acid (SRHA). Results indicate that pH and particle ratios are key to modulating the stability of MoS2/clay systems. At pH 4, aggregation of MoS2 increased with increasing MoS2/clay ratios and approached maximum values of 0.09 and 0.06 nm/s in the binary systems with montmorillonite and kaolinite, respectively. Electrostatic attraction facilitates heteroaggregation at pH values of 4 and 6; differences in the clay structures (i.e., face–face or face–edge aggregates) might explain the resulting MoS2/clay aggregate configurations, which were probed via the evolution of particle size distribution. The presence of only 0.1 mg/L SRHA drastically suppresses the heteroaggregation propensity of MoS2 nanosheets with geocolloids (to less than 0.01 nm/s at all pH values tested). The high stability of these heterogeneous systems under environmentally relevant conditions can increase the likelihood for cellular uptake and long-distance transport of MoS2.
Abstract
The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event‐driven, ...and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain's ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications. To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures. A co‐design vision is described by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks. Finally, a framework is presented to categorize increasingly advanced hardware‐based probabilistic computing technologies.
Ambipolar dual-gate transistors based on low-dimensional materials, such as graphene, carbon nanotubes, black phosphorus, and certain transition metal dichalcogenides (TMDs), enable reconfigurable ...logic circuits with a suppressed off-state current. These circuits achieve the same logical output as complementary metal–oxide semiconductor (CMOS) with fewer transistors and offer greater flexibility in design. The primary challenge lies in the cascadability and power consumption of these logic gates with static CMOS-like connections. In this article, high-performance ambipolar dual-gate transistors based on tungsten diselenide (WSe2) are fabricated. A high on–off ratio of 108 and 106, a low off-state current of 100 to 300 fA, a negligible hysteresis, and an ideal subthreshold swing of 62 and 63 mV/dec are measured in the p- and n-type transport, respectively. We demonstrate cascadable and cascaded logic gates using ambipolar TMD transistors with minimal static power consumption, including inverters, XOR, NAND, NOR, and buffers made by cascaded inverters. A thorough study of both the control gate and the polarity gate behavior is conducted. The noise margin of the logic gates is measured and analyzed. The large noise margin enables the implementation of VT-drop circuits, a type of logic with reduced transistor number and simplified circuit design. Finally, the speed performance of the VT-drop and other circuits built by dual-gate devices is qualitatively analyzed. This work makes advancements in the field of ambipolar dual-gate TMD transistors, showing their potential for low-power, high-speed, and more flexible logic circuits.
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