More Like this

1. Tunneling current-controlled spin states in few-layer van der Waals magnets

   https://doi.org/10.1038/s41467-024-47820-5  

   Fu, ZhuangEn ; Samarawickrama, Piumi I. ; Ackerman, John ; Zhu, Yanglin ; Mao, Zhiqiang ; Watanabe, Kenji ; Taniguchi, Takashi ; Wang, Wenyong ; Dahnovsky, Yuri ; Wu, Mingzhong ; et al ( May 2024 , Nature Communications)

   Effective control of magnetic phases in two-dimensional magnets would constitute crucial progress in spintronics, holding great potential for future computing technologies. Here, we report a new approach of leveraging tunneling current as a tool for controlling spin states in CrI₃. We reveal that a tunneling current can deterministically switch between spin-parallel and spin-antiparallel states in few-layer CrI₃, depending on the polarity and amplitude of the current. We propose a mechanism involving nonequilibrium spin accumulation in the graphene electrodes in contact with the CrI₃layers. We further demonstrate tunneling current-tunable stochastic switching between multiple spin states of the CrI₃tunnel devices, which goes beyond conventional bi-stable stochastic magnetic tunnel junctions and has not been documented in two-dimensional magnets. Our findings not only address the existing knowledge gap concerning the influence of tunneling currents in controlling the magnetism in two-dimensional magnets, but also unlock possibilities for energy-efficient probabilistic and neuromorphic computing.

    

   more » « less
2. MTJ-Based Dithering for Stochastic Analog-to-Digital Conversion

   https://doi.org/10.1109/ISCAS51556.2021.9401632  

   Mitrovic, Ana ; Friedman, Eby G. ( May 2021 , Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS))

   null (Ed.)

   The stochastic behavior of magnetic tunnel junctions (MTJ) finds use in many applications - from analog-to- digital conversion to neuromorphic computing. In this paper, a dithering method exploiting the stochastic behavior of an MTJ, based on the voltage controlled magnetic anisotropy effect, is proposed. This method is used to measure low frequency analog signals over an interval. The circuit is composed of two MTJ devices that convert an analog signal into a series of high resistance and low resistance stochastic states, creating a dithering effect. The behavior of the input signal is extracted from the switching frequency. The binary nature of the output signal reduces the complexity, enabling a small, fast, and energy efficient circuit for extracting different forms of information from an analog signal. 

   more » « less
3. Shape‐Dependent Multi‐Weight Magnetic Artificial Synapses for Neuromorphic Computing

   https://doi.org/10.1002/aelm.202200563  

   Leonard, Thomas ; Liu, Samuel ; Alamdar, Mahshid ; Jin, Harrison ; Cui, Can ; Akinola, Otitoaleke_G ; Xue, Lin ; Xiao, T_Patrick ; Friedman, Joseph_S ; Marinella, Matthew_J ; et al ( September 2022 , Advanced Electronic Materials)

   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
4. High-speed CMOS-free purely spintronic asynchronous recurrent neural network

   https://doi.org/10.1063/5.0129006  

   Mathews, Pranav O. ; Duffee, Christian B. ; Thayil, Abel ; Stovall, Ty E. ; Bennett, Christopher H. ; Garcia-Sanchez, Felipe ; Marinella, Matthew J. ; Incorvia, Jean Anne ; Hassan, Naimul ; Hu, Xuan ; et al ( March 2023 , APL Machine Learning)

   The exceptional capabilities of the human brain provide inspiration for artificially intelligent hardware that mimics both the function and the structure of neurobiology. In particular, the recent development of nanodevices with biomimetic characteristics promises to enable the development of neuromorphic architectures with exceptional computational efficiency. In this work, we propose biomimetic neurons comprised of domain wall-magnetic tunnel junctions that can be integrated into the first trainable CMOS-free recurrent neural network with biomimetic components. This paper demonstrates the computational effectiveness of this system for benchmark tasks and its superior computational efficiency relative to alternative approaches for recurrent neural networks. 

   more » « less
5. Sub-volt switching of nanoscale voltage-controlled perpendicular magnetic tunnel junctions

   https://doi.org/10.1038/s43246-022-00310-x  

   Shao, Yixin ; Lopez-Dominguez, Victor ; Davila, Noraica ; Sun, Qilong ; Kioussis, Nicholas ; Katine, Jordan A. ; Khalili Amiri, Pedram ( December 2022 , Communications Materials)

   Abstract Magnetic random-access memory (MRAM) based on voltage-controlled magnetic anisotropy in magnetic tunnel junctions (MTJs) is a promising candidate for high-performance computing applications, due to its lower power consumption, higher bit density, and the ability to reduce the access transistor size when compared to conventional current-controlled spin-transfer torque MRAM. The key to realizing these advantages is to have a low MTJ switching voltage. Here, we report a perpendicular MTJ structure with a high voltage-controlled magnetic anisotropy coefficient ~130 fJ/Vm and high tunnel magnetoresistance exceeding 150%. Owing to the high voltage-controlled magnetic anisotropy coefficient, we demonstrate sub-nanosecond precessional switching of nanoscale MTJs with diameters of 50 and 70 nm, using a voltage lower than 1 V. We also show scaling of this switching mechanism down to 30 nm MTJs, with voltages close to 2 V. The results pave the path for the future development and application of voltage-controlled MRAMs and spintronic devices in emerging computing systems. 

   more » « less