For the progress of artificial neural networks, the imitation of multiple biological functions is indispensable for processing more tasks in a complex working environment. Memristors, which possess these advantages such as uniformity, high switching speed, and smaller device scale, are the better candidates compared to conventional complementary metal–oxide–semiconductor (CMOS) technology in artificial neural networks. In this work, an Ag/ZrOx/Pt threshold switching memristor (TSM) is designed to overcome the drawback of the large variation in the non‐volatile filament type memristor. The cycle‐to‐cycle and device‐to‐device variations are 5.6% and 4.9%. This device has mimicked the “nociceptive threshold,” “relaxation,” “no adaptation,” and “sensitization” features for the nociceptor which can prevent the artificial intelligence system from dangers in the external environment. Additionally, with the change in the strength of the external stimulus, the artificial neuron is also built by emulating “all‐or‐nothing,” “threshold‐driven‐spiking,” and “strength‐modulated” characteristics. The proposed threshold‐switching memristor allows the simultaneous emulation of the biological nociceptor and leaky integrate‐and‐fire neuron for the first time, which represents an advance in the bioinspired technology adopted in future artificial neural networks and humanoid robots.
In article number
1801354
, Hsing‐Yu Tuan and co‐workers effectively activate red phosphorus as an anode for potassium‐ion batteries with a record‐high specific energy density.
Neuromorphic computing, inspired by the biological neuronal
system,
is a high potential approach to substantially alleviate the cost of
computational latency and energy for massive data processing. Artificial
synapses with regulable synaptic weights are the basis of neuromorphic
computation, providing an efficient and low-power system to overcome
the constraints of the von Neumann architecture. Here, we report an
ITO/TaO
x
-based synaptic capacitor and
transistor. With the drift motion of mobile-charged ions in the TaO
x
, the capacitance and channel conductance can
be tuned to exhibit synaptic weight modulation. Robust stability in
the cycle-to-cycle (C2C) variation is found in capacitance and conductance
potentiation/depression weight updating of 0.9 and 1.8%, respectively.
Simulation results show a higher classification accuracy of handwritten
digit recognition (95%) in capacitance synapses than that in conductance
synapses (84%). Besides, the synaptic capacitor consumes much less
energy than the synaptic transistor. Moreover, the ITO/TaO
x
-based capacitor successfully emulates the pain-perceptual
sensitization on top of the superior performance, indicating its promising
potential in applying the capacitive neural network.
The physical implementation of artificial neural networks, also known as “neuromorphic engineering” as advocated by Carver Mead in the late 1980s, has become urgent because of the increasing demand on massive and unstructured data processing. complementary metal-oxide-semiconductor-based hardware suffers from high power consumption due to the von Neumann bottleneck; therefore, alternative hardware architectures and devices meeting the energy efficiency requirements are being extensively investigated for neuromorphic computing. Among the emerging neuromorphic electronics, oxide-based three-terminal artificial synapses merit the features of scalability and compatibility with the silicon technology as well as the concurrent signal transmitting-and-learning. In this Perspective, we survey four types of three-terminal artificial synapses classified by their operation mechanisms, including the oxide electrolyte-gated transistor, ion-doped oxide electrolyte-gated transistor, ferroelectric-gated transistor, and charge trapping-gated transistor. The synaptic functions mimicked by these devices are analyzed based on the tunability of the channel conductance correlated with the charge relocation and polarization in gate dielectrics. Finally, the opportunities and challenges of implementing oxide-based three-terminal artificial synapses in physical neural networks are delineated for future prospects.
Gradual switching in the memristor or memcapacitor devices is the key parameter for the next generation of bio-inspired neuromorphic computing. Here, we have fabricated the WOx/ZrOx dual-oxide layered device, which shows the coexistence of gradual resistive and capacitive switching arisen from the current and capacitance hysteresis curves, respectively. The expansion of hysteresis loop can be modulated by altering the oxygen content in the oxide materials. Interestingly, the presence of negative differential resistance (NDR) is dependent on the voltage sweep direction and range of applied bias, which can be reasoned by the local electric field, charge trapping/detrapping, and conduction band offset at the dual-oxide interface. This study provides the concept of the coexistence of current and capacitance hysteresis along with NDR, and it is highly potential for memristor and memcapacitor circuits to explore neuromorphic computing.
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