Computing inspired by the human brain requires a massive parallel architecture of low-power consuming elements of which the internal state can be changed. SrTiO3 is a complex oxide that offers rich electronic properties; here Schottky contacts on Nb-doped SrTiO3 are demonstrated as memristive elements for neuromorphic computing. The electric field at the Schottky interface alters the conductivity of these devices in an analog fashion, which is important for mimicking synaptic plasticity. Promising power consumption and endurance characteristics are observed. The resistance states are shown to emulate the forgetting process of the brain. A charge trapping model is proposed to explain the switching behavior.
Memristors have attracted interest as neuromorphic computation elements because they show promise in enabling efficient hardware implementations of artificial neurons and synapses. We performed measurements on interface-type memristors to validate their use in neuromorphic hardware. Specifically, we utilized Nb-doped SrTiO3 memristors as synapses in a simulated neural network by arranging them into differential synaptic pairs, with the weight of the connection given by the difference in normalized conductance values between the two paired memristors. This network learned to represent functions through a training process based on a novel supervised learning algorithm, during which discrete voltage pulses were applied to one of the two memristors in each pair. To simulate the fact that both the initial state of the physical memristive devices and the impact of each voltage pulse are unknown we injected noise into the simulation. Nevertheless, discrete updates based on local knowledge were shown to result in robust learning performance. Using this class of memristive devices as the synaptic weight element in a spiking neural network yields, to our knowledge, one of the first models of this kind, capable of learning to be a universal function approximator, and strongly suggests the suitability of these memristors for usage in future computing platforms.
Ella Gale opened discussion of the introductory lecture by Rainer Waser: At the end of your talk you introduced complementary resistance switches, which you used for binary pattern matching. Do you have any insight into how you might use these systems to do computation in the real number space? Rainer Waser answered: We are currently working on this. (2:[2]2) Ilia Valov said: From your point of view, at the moment, what are the hottest points of research in memristive cells? What are the main challenges? Rainer Waser answered: The hot points of research are the physical understanding of the reliability-limiting processes in conjunction with the material's treasure map and, from the application side, the development of a convincing neuromorphic circuit that shows clear benet compared to standard CMOS solutions (4:[4]4) Hans Hilgenkamp asked: Most memristor embodiments shown in the papers are based on stacked devices with the current ow perpendicular to the plane. Do all applications necessarily favour this type of conguration, or are there also applications for which an in-plane conguration would be preferred? DIS C8FD90059K
The areal footprint of memristors is a key consideration in material‐based neuromorphic computing and large‐scale architecture integration. Electronic transport in the most widely investigated memristive devices is mediated by filaments, posing a challenge to their scalability in architecture implementation. Here, a compelling alternative memristive device is presented and it is demonstrated that areal downscaling leads to enhancement in the memristive memory window, while maintaining analog behavior, contrary to expectations. The device designs directly integrated on semiconducting Nb‐doped SrTiO3 (Nb:STO) allows leveraging electric field effects at edges, increasing the dynamic range in smaller devices. The findings are substantiated by studying the microscopic nature of switching using scanning transmission electron microscopy, in different resistive states, revealing an interfacial layer whose physical extent is influenced by applied electric fields. The ability of Nb:STO memristors to satisfy hardware and software requirements with downscaling, while significantly enhancing memristive functionalities, make them strong contenders for non‐von‐Neumann computing, beyond complementary metal–oxide–semiconductor.
Spintronics-based nonvolatile components in neuromorphic circuits offer the possibility of realizing novel functionalities at low power. Current-controlled electrical switching of magnetization is actively researched in this context. Complex oxide heterostructures with perpendicular magnetic anisotropy (PMA), consisting of SrRuO3 (SRO) grown on SrTiO3 (STO) are strong material contenders. Utilizing the crystal orientation, magnetic anisotropy in such simple heterostructures can be tuned to either exhibit a perfect or slightly tilted PMA. Here, we investigate current induced magnetization modulation in such tailored ferromagnetic layers with a material with strong spin-orbit coupling (Pt), exploiting the spin Hall effect. We find significant differences in the magnetic anisotropy between the SRO/STO heterostructures, as manifested in the first and second harmonic magnetoresistance measurements. Current-induced magnetization switching can be realized with spin-orbit torques, but for systems with perfect PMA this switching is probabilistic as a result of the high symmetry. Slight tilting of the PMA can break this symmetry and allow the realization of deterministic switching. Control over the magnetic anisotropy of our heterostructures therefore provides control over the manner of switching. Based on our findings, we propose a three-terminal spintronic memristor, with a magnetic tunnel junction design, that shows several resistive states controlled by electric charge. Non-volatile states can be written through SOT by applying an in-plane current, and read out as a tunnel current by applying a small out-of-plane current. Depending on the anisotropy of the SRO layer, the writing mechanism is either deterministic or probabilistic allowing for different functionalities to emerge. We envisage that the probabilistic MTJs could be used as synapses while the deterministic devices can emulate neurons.
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