In neuromorphic computing, artificial synapses provide a multi‐weight (MW) conductance state that is set based on inputs from neurons, analogous to the brain. Herein, artificial synapses based on magnetic materials that use a magnetic tunnel junction (MTJ) and a magnetic domain wall (DW) are explored. By fabricating lithographic notches in a DW track underneath a single MTJ, 3–5 stable resistance states that can be repeatably controlled electrically using spin‐orbit torque are achieved. The effect of geometry on the synapse behavior is explored, showing that a trapezoidal device has asymmetric weight updates with high controllability, while a rectangular device has higher stochasticity, but with stable resistance levels. The device data is input into neuromorphic computing simulators to show the usefulness of application‐specific synaptic functions. Implementing an artificial neural network (NN) applied to streamed Fashion‐MNIST data, the trapezoidal magnetic synapse can be used as a metaplastic function for efficient online learning. Implementing a convolutional NN for CIFAR‐100 image recognition, the rectangular magnetic synapse achieves near‐ideal inference accuracy, due to the stability of its resistance levels. This work shows MW magnetic synapses are a feasible technology for neuromorphic computing and provides design guidelines for emerging artificial synapse technologies.more » « less
- NSF-PAR ID:
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Electronic Materials
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) makes SNNs promising for edge applications that require high energy efficiency. To realize SNNs in hardware, spintronic neuron implementations can bring advantages of scalability and energy efficiency. Domain wall (DW)-based magnetic tunnel junction (MTJ) devices are well suited for probabilistic neural networks given their intrinsic integrate-and-fire behavior with tunable stochasticity. Here, we present a scaled DW-MTJ neuron with voltage-dependent firing probability. The measured behavior was used to simulate a SNN that attains accuracy during learning compared to an equivalent, but more complicated, multi-weight DW-MTJ device. The validation accuracy during training was also shown to be comparable to an ideal leaky integrate and fire device. However, during inference, the binary DW-MTJ neuron outperformed the other devices after Gaussian noise was introduced to the Fashion-MNIST classification task. This work shows that DW-MTJ devices can be used to construct noise-resilient networks suitable for neuromorphic computing on the edge.more » « less
null (Ed.)We propose energy-efficient voltage-induced strain control of a domain wall (DW) in a perpendicularly magnetized nanoscale racetrack on a piezoelectric substrate that can implement a multistate synapse to be utilized in neuromorphic computing platforms. Here, strain generated in the piezoelectric is mechanically transferred to the racetrack and modulates the perpendicular magnetic anisotropy (PMA) in a system that has significant interfacial Dzyaloshinskii-Moriya interaction (DMI). When different voltages are applied (i.e., different strains are generated) in conjunction with spin-orbit torque (SOT) due to a fixed current flowing in the heavy metal layer for a fixed time, DWs are translated to different distances and implement different synaptic weights. We have shown using micromagnetic simulations that five-state and three-state synapses can be implemented in a racetrack that is modeled with the inclusion of natural edge roughness and room temperature thermal noise. These simulations show interesting dynamics of DWs due to interaction with roughness-induced pinning sites. Thus, notches need not be fabricated to implement multistate nonvolatile synapses. Such a strain-controlled synapse has an energy consumption of ~1 fJ and could thus be very attractive to implement energy-efficient quantized neural networks, which has been shown recently to achieve near equivalent classification accuracy to the full-precision neural networks.more » « less
Neuromorphic computing has the great potential to enable faster and more energy‐efficient computing by overcoming the von Neumann bottleneck. However, most emerging nonvolatile memory (NVM)‐based artificial synapses suffer from insufficient precision, nonlinear synaptic weight update, high write voltage, and high switching latency. Moreover, the spatiotemporal dynamics, an important temporal component for cognitive computing in spiking neural networks (SNNs), are hard to generate with existing complementary metal–oxide–semiconductor (CMOS) devices or emerging NVM. Herein, a three‐terminal, Li
xWO3‐based electrochemical synapse (LiWES) is developed with low programming voltage (0.2 V), fast programming speed (500 ns), and high precision (1024 states) that is ideal for artificial neural networks applications. Time‐dependent synaptic functions such as paired‐pulse facilitation (PPF) and temporal filtering that are critical for SNNs are also demonstrated. In addition, by leveraging the spike‐encoded timing information extracted from the short‐term plasticity (STP) behavior in the LiWES, an SNNs model is built to benchmark the pattern classification performance of the LiWES, and the result indicates a large boost in classification performance (up to 128×), compared with those NO‐STP synapses.
Resistive switching is a promising technology for artificial synapses, the most critical component and building block of a neural network for brain-inspired neuromorphic computing. The artificial synapse is capable of emulating a signal process and memory functions of biological synapses. The artificial synapse fabricated by natural bioorganic materials is essential for developing soft, flexible, and biocompatible electronics and sustainable, biodegradable, and environmentally friendly neuromorphic systems. In this work, a natural biomaterial—honey based resistive switching device—was demonstrated to emulate some important functionalities of biological synapses, including synaptic potentiation and depression, short-term and long-term memory, spatial summation, and shunting inhibition. The results indicate the potential of honey based resistive switching for artificial synaptic devices in renewable neuromorphic systems and bioelectronics.
New parallel computing architectures based on neuromorphic computing are needed due to their advantages over conventional computation with regards to real‐time processing of unstructured sensory data such as image, video, or voice. However, developing artificial neuromorphic system remains a challenge due to the lack of electronic synaptic devices, which can mimic all the functions of biological synapses with low energy consumption. Here it is reported that two‐terminal organometal trihalide perovskite (OTP) synaptic devices can mimic the neuromorphic learning and remembering process. Various functions known in biological synapses are demonstrated in OTP synaptic devices including four forms of spike‐timing‐dependent plasticity (STDP), spike‐rate‐dependent plasticity (SRDP), short‐term plasticity (STP) and long‐term potentiation (LTP)), and learning‐experience behavior. The excellent photovoltaic property of the OTP devices also enables photo‐read synaptic functions. The perovskite synapse has the potential of low energy consumption of femto‐Joule/(100 nm)2per event, which is close to the energy consumption of biological synapses. The demonstration of energy‐efficient OTP synaptic devices opens a new plausible application of OTP materials into neuromorphic devices, which offer the high connectivity and high density required for biomimic computing.