ترغب بنشر مسار تعليمي؟ اضغط هنا

Scalable Memdiodes Exhibiting Rectification and Hysteresis for Neuromorphic Computing

109   0   0.0 ( 0 )
 نشر من قبل M. Brooks Tellekamp Jr.
 تاريخ النشر 2019
  مجال البحث فيزياء
والبحث باللغة English




اسأل ChatGPT حول البحث

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.

قيم البحث

اقرأ أيضاً

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 an d 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.
Ferroelectric tunnel junctions (FTJ) based on hafnium zirconium oxide (Hf1-xZrxO2; HZO) are a promising candidate for future applications, such as low-power memories and neuromorphic computing. The tunneling electroresistance (TER) is tunable through the polarization state of the HZO film. To circumvent the challenge of fabricating thin ferroelectric HZO layers in the tunneling range of 1-3 nm range, ferroelectric/dielectric double layer sandwiched between two symmetric metal electrodes are used. Due to the decoupling of the ferroelectric polarization storage layer and a dielectric tunneling layer with a higher bandgap, a significant TER ratio between the two polarization states is obtained. By exploiting previously reported switching behaviour and the gradual tunability of the resistance, FTJs can be used as potential candidates for the emulation of synapses for neuromorphic computing in spiking neural networks. The implementation of two major components of a synapse are shown: long term depression/potentiation by varying the amplitude/width/number of voltage pulses applied to the artificial FTJ synapse, and spike-timing-dependent-plasticity curves by applying time-delayed voltages at each electrode. These experimental findings show the potential of spiking neural networks and neuromorphic computing that can be implemented with hafnia-based FTJs.
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 pr operties 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.
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.
Graphene plasmonic resonators have been broadly studied in the terahertz and mid-infrared ranges because of their electrical tunability and large confinement factors which can enable dramatic enhancement of light-matter coupling. In this work, we dem onstrate that the characteristic scaling laws of graphene plasmons change for smaller (< 40 nm) plasmonic wavelengths, expanding the operational frequencies of graphene plasmonic resonators into the near-infrared (NIR) and modifying their optical confinement properties. We utilize a novel bottom-up block copolymer lithography method that substantially improves upon top-down methods to create resonators as narrow as 12 nm over centimeter-scale areas. Measurements of these structures reveal that their plasmonic resonances are strongly influenced by non-local and quantum effects, which push their resonant frequency into the NIR (2.2 um), almost double the frequency of previous experimental works. The confinement factors of these resonators, meanwhile, reach 137 +/- 25, amongst the largest reported in literature for an optical cavity. While our findings indicate that the enhancement of some forbidden transitions are an order of magnitude weaker than predicted, the combined NIR response and large confinement of these structures make them an attractive platform to explore ultra-strongly enhanced spontaneous emission.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا