Self-Reconfigurable Memristor-Based Computing Architecture: Design, Fabrication, and Characterization

ABSTRACT: The behavior of the Chalcogenide based ion-conducting memristor lends itself for use as an element in a simple neuromorphic computing circuit. The reaction of a circuit to an external stimulus may be the result of its ability to learn from previous similar, yet unrelated exposures to environmental stimulus. A highly specialized variation of the memristor, previously developed by the Advanced Memory and Reconfigurable Logic Group at Boise State University, led by Dr. Kris Campbell, PhD. will be leveraged as the key functional component in a family of neuromorphic circuits. The electrical memristor device has demonstrated predictable discrete states, hysteresis, and time dependent memory, each of which may be leveraged for functional decision behaviors. In essence, the device remembers past stimulus, has a current state that is a direct function of both the past stimulus and the current stimulus, and eventually forgets, depending on the strength of the programming state and the passage of time. These unique device characterisics enable a new classification of intellegent computing architectures that may have the potential to demonstrate rudimentary adaptive learning behaviors similar to those found in nature. BENEFIT: The research supported will enable the design and prototyping of functional memristor based neuromorphic circuits which may potentually revolutionize the electronics industry by introducing new methodologies facilitating neuromorphic computing. These memristor based circuits will be demonstrated and integrated within existing technology frameworks, establishing the feasibility of manufacture within current technology nodes. The IP cores and demonstration vehicles developed during the course of this work are directly followed by a commercialization strategy to quickly expand the fledgling memristor efforts in industry. The result is intended to enable ground breaking applications and exploration in neuromorphic computing and reconfigurable electronics.