Samiran Ganguly

Modular Approach to Spintronics

There has been enormous progress in the last two decades, effectively combining spintronics and magnetics into a powerful force that is shaping the field of memory devices. New materials and phenomena continue to be discovered at an impressive rate, providing an ever-increasing set of building blocks that could be exploited in designing transistor-like functional devices of the future. The objective of this paper is to provide a quantitative foundation for this building block approach, so that new discoveries can be integrated into functional device concepts, quickly analyzed and critically evaluated. Through careful benchmarking against available theory and experiment we establish a set of elemental modules representing diverse materials and phenomena. These elemental modules can be integrated seamlessly to model composite devices involving both spintronic and nanomagnetic phenomena. We envision the library of modules to evolve both by incorporating new modules and by improving existing modules as the field progresses. The primary contribution of this paper is to establish the ground rules or protocols for a modular approach that can build a lasting bridge between materials scientists and circuit designers in the field of spintronics and nanomagnetics.

Physics-based factorization of Magnetic Tunnel Junctions for modeling and circuit simulation

We present a physics-based factorization of Magnetic Tunnel Junctions (MTJ) in terms of a minimal number of experimentally and theoretically accessible parameters that can be used to optimize existing MTJ designs as well as to probe emerging MTJ devices. Our model fully captures angular/voltage dependence of state-of-the-art MTJs and Spin Valves (SV) and is compatible with existing circuit simulation frameworks such as Verilog-A and SPICE.

Evaluating Spintronic Devices Using the Modular Approach

Over the past decade, a large family of spintronic devices has been proposed as candidates for replacing CMOS for future digital logic circuits. Using the recently developed modular approach framework, we investigate and identify the physical bottlenecks and engineering challenges facing current spintronic devices. We then evaluate how systematic advancements in material properties and device design innovations impact the performance of spintronic devices, as a possible continuation of Moore’s Law, even though some of these projections are speculative and may require technological breakthroughs. Finally, we illustrate the use of the modular approach as an exploratory tool for probabilistic networks, using superparamagnetic magnets as building blocks for such networks. These building blocks leverage the inherent physics of stochastic spin-torque switching and could provide ultracompact and efficient hardware for beyond-Boolean computational paradigms.

Hardware based spatio-temporal neural processing backend for imaging sensors: Towards a smart camera

In this work we show how we can build a technology platform for cognitive imaging sensors using recent advances in recurrent neural network architectures and training methods inspired from biology. We demonstrate learning and processing tasks specific to imaging sensors, including enhancement of sensitivity and signal-to-noise ratio (SNR) purely through neural filtering beyond the fundamental limits sensor materials, and inferencing and spatio-temporal pattern recognition capabilities of these networks with applications in object detection, motion tracking and prediction. We then show designs of unit hardware cells built using complementary metal-oxide semiconductor (CMOS) and emerging materials technologies for ultra-compact and energy-efficient embedded neural processors for smart cameras.

Energy-delay-reliability of present and next generation STT-RAM technology

STT-RAMs show the promise to be the universal memory device with applications in embedded devices. There are outstanding challenges that need to be addressed before a wide-scale adoption of this technology happens. The solution to these challenges lie in integration of emerging high performance spintronic materials as well as clever circuit based techniques to operate these devices at their peak performance. In this work we present a material-device-circuit co-design framework that connects the properties of materials and transport physics to circuits and systems performance. To illustrate the use of this framework we study present and next generation STT-RAM technology in terms of energy-delay-reliability performance metrics and suggest possible directions for future generation devices.

From materials to systems: a multiscale analysis of nanomagnetic switching

With the increasing demand for low-power electronics, nanomagnetic devices have emerged as strong potential candidates to complement present day transistor technology. A variety of novel switching effects such as spin torque and giant spin Hall offer scalable ways to manipulate nanosized magnets. However, the low intrinsic energy cost of switching spins is often compromised by the energy consumed in the overhead circuitry in creating the necessary switching fields. Scaling brings in added concerns such as the ability to distinguish states (readability) and to write information without spontaneous backflips (reliability). A viable device must ultimately navigate a complex multi-dimensional material and design space defined by volume, energy budget, speed, and a target read–write–retention error. In this paper, we review the major challenges facing nanomagnetic devices and present a multiscale computational framework to explore possible innovations at different levels (material, device, or circuit), along with a holistic understanding of their overall energy delay–reliability trade-off.