Doctor of Philosophy
Gary M. Atkinson
Shiv N. Khanna
Research in future generation computing is focused on reducing energy dissipation while maintaining the switching speed in a binary operation to continue the current trend of increasing transistor-density according to Moore’s law. Unlike charge-based CMOS technology, spin-based nanomagnetic technology, based on switching bistable magnetization of single domain shape-anisotropic nanomagnets, has the potential to achieve ultralow energy dissipation due to the fact that no charge motion is directly involved in switching. However, switching of magnetization has not been any less dissipative than switching transistors because most magnet switching schemes involve generating a current to produce a magnetic field, or spin transfer torque or domain wall motion to switch magnetization. Current-induced switching invariably dissipates an exorbitant amount of energy in the switching circuit that nullifies any energy advantage that a magnet may have over a transistor. Magnetoelastic switching (switching the magnetization of a magnetostrictive magnet with voltage generated stress) is an unusual switching paradigm where the dissipation turns out to be merely few hundred kT per switching event – several orders of magnitude less than that encountered in current-based switching. A fundamental obstacle, though, is to deterministically switch the magnetization of a nanomagnet between two stable states that are mutually anti-parallel with stress alone. In this work, I have investigated ways to mitigate this problem.
One popular approach to flip the magnetizations of a nanomagnet is to pass a spin polarized current through it that transfers spin angular moment from the current to the electrons in the magnet, thereby switching their spins and ultimately the magnet’s magnetization. This approach – known as spin transfer torque (STT) – is very dissipative because of the enormous current densities needed to switch magnets, We, therefore, devised a mixed mode technique to switch magnetization with a combination of STT and stress to gain both energy efficiency from
stress and deterministic 180o switching from STT. This approach reduces the total energy dissipation by roughly one order of magnitude. We then extended this idea to find a way to deterministically flip magnetization with stress alone. Sequentially applying stresses along two skewed axes, a complete 180o switching can be achieved. These results have been verified with stochastic Landau-Lifshitz-Gilbert simulation in the presence of thermal noise. The 180o switching makes it possible to develop a genre of magneto-elastic memory where bits are written entirely with voltage generated stress with no current flow. They are extremely energy-efficient.
In addition to memory devices, a universal NAND logic device has been proposed which satisfies all the essential characteristics of a Boolean logic gate. It is non-volatile unlike transistor based logic gates in the sense that that gate can process binary inputs and store the output (result) in the magnetization states of magnets, thereby doubling as both logic and memory. Such dual role elements can spawn non-traditional non-von-Neumann architectures without the processor and memory partition that reduces energy efficiency and introduces additional errors. A bit comparator is also designed, which happens to be all straintronic, yet reconfigurable. Moreover, a straintronic spin neuron is designed for neural computing architecture that dissipates orders of magnitude less energy than its CMOS based counterparts.
Finally, an experiment has been performed to demonstrate a complete 180o switching of magnetization in a shape anisotropic magnetostrictive Co nanomagnet using voltage generated stress. The device is synthesized with nano-fabrication techniques namely electron beam lithography, electron beam evaporation, and lift off. The experimental results vindicate our proposal of applying sequential stress along two skewed axes to reverse magnetization with stress and therefore, provide a firm footing to magneto-elastic memory technology.
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