Exploring Spin-transfer-torque devices and memristors for logic and memory applications

As scaling CMOS devices is approaching its physical limits, researchers have begun exploring newer devices and architectures to replace CMOS. Due to their non-volatility and high density, Spin Transfer Torque (STT) devices are among the most prominent candidates for logic and memory applications. In this research, we first considered a new logic style called All Spin Logic (ASL). Despite its advantages, ASL consumes a large amount of static power; thus, several optimizations can be performed to address this issue. We developed a systematic methodology to perform the optimizations to ensure stable operation of ASL. Second, we investigated reliable design of STT-MRAM bit-cells and addressed the conflicting read and write requirements, which results in overdesign of the bit-cells. Further, a Device/Circuit/Architecture co-design framework was developed to optimize the STT-MRAM devices by exploring the design space through jointly considering yield enhancement techniques at different levels of abstraction. Recent advancements in the development of memristive devices have opened new opportunities for hardware implementation of non-Boolean computing. To this end, the suitability of memristive devices for swarm intelligence algorithms has enabled researchers to solve a maze in hardware. In this research, we utilized swarm intelligence of memristive networks to perform image edge detection. First, we proposed a hardware-friendly algorithm for image edge detection based on ant colony. Next, we designed the image edge detection algorithm using memristive networks

What constitutes a nanoswitch? A Perspective

Progress in the last two decades has effectively integrated spintronics and nanomagnetics into a single field, creating a new class of spin-based devices that are now being used both to Read (R) information from magnets and to Write (W) information onto magnets. Many other new phenomena are being investigated for nano-electronic memory as described in Part II of this book. It seems natural to ask whether these advances in memory devices could also translate into a new class of logic devices. What makes logic devices different from memory is the need for one device to drive another and this calls for gain, directionality and input-output isolation as exemplified by the transistor. With this in mind we will try to present our perspective on how W and R devices in general, spintronic or otherwise, could be integrated into transistor-like switches that can be interconnected to build complex circuits without external amplifiers or clocks. We will argue that the most common switch used to implement digital logic based on complementary metal oxide semiconductor (CMOS) transistors can be viewed as an integrated W-R unit having an input-output asymmetry that give it gain and directionality. Such a viewpoint is not intended to provide any insight into the operation of CMOS switches, but rather as an aid to understanding how W and R units based on spins and magnets can be combined to build transistor-like switches. Next we will discuss the standard W and R units used for magnetic memory devices and present one way to integrate them into a single unit with the input electrically isolated from the output. But we argue that this integrated W-R unit would not provide the key property of gain. We will then show that the recently discovered giant spin Hall effect could be used to construct a W-R unit with gain and suggest other possibilities for spin switches with gain.Comment: 27 pages. To appear in Emerging Nanoelectronic Devices, Editors: An Chen, James Hutchby, Victor Zhirnov and George Bourianoff, John Wiley & Sons (to be published

Stochastic Spin-Orbit Torque Devices as Elements for Bayesian Inference

Probabilistic inference from real-time input data is becoming increasingly popular and may be one of the potential pathways at enabling cognitive intelligence. As a matter of fact, preliminary research has revealed that stochastic functionalities also underlie the spiking behavior of neurons in cortical microcircuits of the human brain. In tune with such observations, neuromorphic and other unconventional computing platforms have recently started adopting the usage of computational units that generate outputs probabilistically, depending on the magnitude of the input stimulus. In this work, we experimentally demonstrate a spintronic device that offers a direct mapping to the functionality of such a controllable stochastic switching element. We show that the probabilistic switching of Ta/CoFeB/MgO heterostructures in presence of spin-orbit torque and thermal noise can be harnessed to enable probabilistic inference in a plethora of unconventional computing scenarios. This work can potentially pave the way for hardware that directly mimics the computational units of Bayesian inference