Recent experiments in spintronics have revealed the opportunity of mimicking brain-like functionalities in a single device structure that can be operated at very low terminal voltages. These non-volatile memory technologies can be arranged in crossbar array fashion to realize In-Memory Computing in neuromorphic systems by application of Kirchoff's law. Spintronics enabled neuromorphic computing has the potential of enabling a significant increase in efficiency of machine learning hardware. However, experimental investigations are still in the infancy stage. The goal of this project is to experimentally demonstrate the emulation of the computational primitives by mimicking biological brain in the underlying hardware substrate exploiting the functional properties of Spin-Orbit Coupling. The EAGER program is focused to prove the feasibility of experimentally demonstrating spin-based neuromorphic devices that has the potential to enable significant improvements in performance and energy consumption as compared to Complementary Metal Oxide Semiconductor technologies. If successful, the research can lead to the development of computing paradigms that inherently exploits nanomagnetic devices as a mechanism to build brain-like hardware. Graduate students and undergraduates from Penn State's Schreyer Honors College will be engaged in the research activities of this project. The PI plans to integrate the results from this project into the Electrical Engineering departmental K-12 summer camp.
Prior experimental efforts have mainly focused on single device characterizations without addressing the issues of scalability and system-level demonstrations. This proposal aims to bridge that gap through the following research thrusts: (i) Fabrication and characterization of heavy-metal based Hall-bar structures at room temperatures to experimentally demonstrate the feasibility of various neural and synaptic functionalities including leaky-spiking neuron, stochastic-spiking neuron, spike-timing dependent plasticity, and short-term plasticity. The device structure will consist of a ferromagnet (CoFe/CoFeB) – heavy metal (Ta/Pt) Hall-bar where current flowing through the device will be used to switch the magnet and the final state will be detected using anomalous Hall effect. (ii) The device will be characterized for performance and non-idealities like magnetization pinning, stochastic programming, thermal noise induced switching, and programming resolution. (iii) Arrays of such devices will be fabricated to investigate the impact of device-to-device and cycle-to-cycle variations and demonstrate a prototype computing system. Efforts will also be placed to develop realistic device models by incorporating device constraints and non-idealities that can be used to predict performance of large-scale systems. Successful completion offers the basis for transformative improvements in the efficiency of machine learning platforms having the capability of performing real-time decision-making in autonomous systems.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
