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posted on 08.05.2020, 01:19 by Mei-Chin Chen

Process variations and increasing leakage current are major challenges toward memory realization in deeply-scaled CMOS devices. Spintronic devices recently emerged as one of the leading candidates for future information storage due to its potential for non-volatility, high speed, low power and good endurance. In this thesis, we start with the basic concepts and applications of three spintronic devices, namely spin or- bit torque (SOT) based spin-valves, SOT-based magnetic tunnel junctions and the magnetic skyrmion (MS) for both logic and machine learning hardware.

We propose a new Spin-Orbit Torque based Domino-style Spin Logic (SOT-DSL) that operates in a sequence of Preset and Evaluation modes of operations. During the preset mode, the output magnet is clocked to its hard-axis using spin Hall effect. In the evaluation mode, the clocked output magnet is switched by a spin current from the preceding stage. The nano-magnets in SOT-DSL are always driven by orthogonal spins rather than collinear spins, which in turn eliminates the incubation delay and allows fast magnetization switching. Based on our simulation results, SOT-DSL shows up to 50% improvement in energy consumption compared to All-Spin Logic. Moreover, SOT-DSL relaxes the requirement for buffer insertion between long spin channels, and significantly lowers the design complexity. This dissertation also covers two applications using MS as information carriers. MS has been shown to possess several advantages in terms of unprecedented stability, ultra-low depinning current density, and compact size.

We propose a multi-bit MS cell with appropriate peripheral circuits. A systematic device-circuit-architecture co-design is performed to evaluate the feasibility of using MS-based memory as last-level caches for general purpose processors. To further establish the viability of skyrmions for other applications, a deep spiking neural network (SNN) architecture where computation units are realized by MS-based devices is also proposed. We develop device architectures and models suitable for neurons and synapses, provide device-to-system level analysis for the design of an All-Spin Spiking Neural Network based on skyrmionic devices, and demonstrate its efficiency over a corresponding CMOS implementation.

Apart from the aforementioned applications such as memory storage elements or logic operation, this research also focuses on the implementation of spin-based device to solve combinatorial optimization problems. Finding an efficient computing method to solve these problems has been researched extensively. The computational cost for such optimization problems exponentially increases with the number of variables using traditional von-Neumann architecture. Ising model, on the other hand, has been proposed as a more suitable computation paradigm for its simple architecture and inherent ability to efficiently solve combinatorial optimization problems. In this work, SHE-MTJs are used as a stochastic switching bit to solve these problems based on the Ising model. We also design an unique approach to map bi-prime factorization problem to our proposed device-circuit configuration. By solving coupled Landau- Lifshitz-Gilbert equations, we demonstrate that our coupling network can factorize up to 16-bit binary numbers.


Degree Type

Doctor of Philosophy


Electrical and Computer Engineering

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Dr. Kaushik Roy

Additional Committee Member 2

Dr. Anand Raghunathan

Additional Committee Member 3

Dr. Vijay Raghunathan

Additional Committee Member 4

Dr. Zhihong Chen