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Register a new userMemristive control of mutual SHNO synchronization for neuromorphic computing
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Added by
Himanshu Fulara Dr.
Publication date
2020
fields
Physics
and research's language is
English
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Synchronization of large spin Hall nano-oscillators (SHNO) arrays is an appealing approach toward ultra-fast non-conventional computing based on nanoscale coupled oscillator networks. However, for large arrays, interfacing to the network, tuning its individual oscillators, their coupling, and providing built-in memory units for training purposes, remain substantial challenges. Here, we address all these challenges using memristive gating of W/CoFeB/MgO/AlOx based SHNOs. In its high resistance state (HRS), the memristor modulates the perpendicular magnetic anisotropy (PMA) at the CoFeB/MgO interface purely by the applied electric field. In its low resistance state (LRS), and depending on the voltage polarity, the memristor adds/subtracts current to/from the SHNO drive. The operation in both the HRS and LRS affects the SHNO auto-oscillation mode and frequency, which can be tuned up to 28 MHz/V. This tuning allows us to reversibly turn on/off mutual synchronization in chains of four SHNOs. We also demonstrate two individually controlled memristors to tailor both the coupling strength and the frequency of the synchronized state. Memristor gating is therefore an efficient approach to input, tune, and store the state of the SHNO array for any non-conventional computing paradigm, all in one platform.
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Neuromorphic computing uses brain-inspired principles to design circuits that can perform computational tasks with superior power efficiency to conventional computers. Approaches that use traditional electronic devices to create artificial neurons and synapses are, however, currently limited by the energy and area requirements of these components. Spintronic nanodevices, which exploit both the magnetic and electrical properties of electrons, can increase the energy efficiency and decrease the area of these circuits, and magnetic tunnel junctions are of particular interest as neuromorphic computing elements because they are compatible with standard integrated circuits and can support multiple functionalities. Here we review the development of spintronic devices for neuromorphic computing. We examine how magnetic tunnel junctions can serve as synapses and neurons, and how magnetic textures, such as domain walls and skyrmions, can function as neurons. We also explore spintronics-based implementations of neuromorphic computing tasks, such as pattern recognition in an associative memory, and discuss the challenges that exist in scaling up these systems.
With many fantastic properties, memristive devices have been proposed as top candidate for next-generation memory and neuromorphic computing chips. Significant research progresses have been made in improving performance of individual memristive devices and in demonstrating functional applications based on small-scale memristive crossbar arrays. However, practical deployment of large-scale traditional metal oxides based memristive crossbar array has been challenging due to several issues, such as high-power consumption, poor device reliability, low integration density and so on. To solve these issues, new materials that possess superior properties are required. Two-dimensional (2D) layered materials exhibit many unique physical properties and show great promise in solving these challenges, further providing new opportunities to implement practical applications in neuromorphic computing. Here, recent research progress on 2D layered materials based memristive device applications is reviewed. We provide an overview of the progresses and challenges on how 2D layered materials are used to solve the issues of conventional memristive devices and to realize more complex functionalities in neuromorphic computing. Besides, we also provide an outlook on exploitation of unique properties of 2D layered materials and van der Waals heterostructures for developing new types of memristive devices and artificial neural mircrocircuits.
Since the experimental discovery of magnetic skyrmions achieved one decade ago, there have been significant efforts to bring the virtual particles into all-electrical fully functional devices, inspired by their fascinating physical and topological properties suitable for future low-power electronics. Here, we experimentally demonstrate such a device: electrically-operating skyrmion-based artificial synaptic device designed for neuromorphic computing. We present that controlled current-induced creation, motion, detection and deletion of skyrmions in ferrimagnetic multilayers can be harnessed in a single device at room temperature to imitate the behaviors of biological synapses. Using simulations, we demonstrate that such skyrmion-based synapses could be used to perform neuromorphic pattern-recognition computing using handwritten recognition data set, reaching to the accuracy of ~89 percents, comparable to the software-based training accuracy of ~94 percents. Chip-level simulation then highlights the potential of skyrmion synapse compared to existing technologies. Our findings experimentally illustrate the basic concepts of skyrmion-based fully functional electronic devices while providing a new building block in the emerging field of spintronics-based bio-inspired computing.
The development of memristive device technologies has reached a level of maturity to enable the design of complex and large-scale hybrid memristive-CMOS neural processing systems. These systems offer promising solutions for implementing novel in-memory computing architectures for machine learning and data analysis problems. We argue that they are also ideal building blocks for the integration in neuromorphic electronic circuits suitable for ultra-low power brain-inspired sensory processing systems, therefore leading to the innovative solutions for always-on edge-computing and Internet-of-Things (IoT) applications. Here we present a recipe for creating such systems based on design strategies and computing principles inspired by those used in mammalian brains. We enumerate the specifications and properties of memristive devices required to support always-on learning in neuromorphic computing systems and to minimize their power consumption. Finally, we discuss in what cases such neuromorphic systems can complement conventional processing ones and highlight the importance of exploiting the physics of both the memristive devices and of the CMOS circuits interfaced to them.
Metal-Nb$_{2}$O$_{5-x}$-metal memdiodes exhibiting rectification, hysteresis, and capacitance are demonstrated for applications in neuromorphic circuitry. These devices do not require any post-fabrication treatments such as filament creation by electroforming that would impede circuit scalability. Instead these devices operate due to Poole-Frenkel defect controlled transport where the high defect density is inherent to the Nb$_{2}$O$_{5-x}$ deposition rather than post-fabrication treatments. Temperature dependent measurements reveal that the dominant trap energy is 0.22 eV suggesting it results from the oxygen deficiencies in the amorphous Nb$_{2}$O$_{5-x}$. Rectification occurs due to a transition from thermionic emission to tunneling current and is present even in thick devices (> 100 nm) due to charge trapping which controls the tunneling distance. The turn-on voltage is linearly proportional to the Schottky barrier height and, in contrast to traditional metal-insulator-metal diodes, is logarithmically proportional to the device thickness. Hysteresis in the I-V curve occurs due to the current limited filling of traps.
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