PhD_dissertation_Punya_final.pdf (7.01 MB)

# Spintronic Devices as P-bits for Probabilistic Computing

Several beyond-CMOS computing technologies have emerged in the recent years to tackle the modern computing tasks that become intractable for Boolean logic based computation, performed on a von Neumann computer. The underlying philosophy in developing such technologies is to harness the natural physics of the computing elements to perform certain specialized computing tasks. One such beyond-CMOS computing paradigm- probabilistic computing is based on a "p-bit" that randomly fluctuates between 0 and 1, a behavior that is naturally mimicked by thermally unstable nanomagnets. A coupled network of such nanomagnets traverses through its collective states and is naturally guided towards the pre-designed low energy states. This property has been shown to be useful in providing hardware acceleration to a wide variety of problems in optimization, invertible logic, inference and machine learning. In order to develop practical circuits with p-bits, an efficient way to implement them in hardware by leveraging spintronics technology is required and forms the subject of this thesis. First, the experiments demonstrating the convergence of a weakly coupled nanomagnet network’s configuration towards the ground state of the associated Hamiltonian is shown. Next, it is demonstrated that by varying the interconnection strength and bias parameters in a two p-bit electrical circuit, Bayesian network building blocks can be implemented in hardware. Following this, a unique p-bit design based on the interaction of spin orbit torque on weak perpendicular anisotropy nanomagnets is presented and its interesting properties such as stochastic resonance, electrically tunable fluctuation rate and correlated fluctuations of two such devices are discussed. As related work, a prototype spin logic device is demonstrated using a composite stack of stable nanomagnets having perpendicular and in plane anisotropies. Finally, the development of a hybrid material stack with greatly improved giant spin Hall efficiency by incorporating WSe

_{2}for energy efficient spin orbit torque switching of nanomagnets is presented.## History

## Degree Type

- Doctor of Philosophy

## Department

- Electrical and Computer Engineering

## Campus location

- West Lafayette