An overarching theme for this research is materials development to enable more precise control over the memristor switching properties, electrical testing results from device pairs that exhibit multi-terminal latching, and efforts towards integration of multiple devices to emulate neuron functions such as programmable spiking behavior. The ultimate goal of this research program is the realization of a memristor-based, fully non-digital, neuron equivalent that can function as a unit cell in a cellular neural network. Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. Different types of NVM devices – including phase change memory, conductive-bridging RAM, filamentary and non-filamentary RRAM, and other NVMs –for use within a neuromorphic computing application would be investigated in this research. Specific research would look into synthesis by Atomic Layer Deposition and Pulsed Laser Deposition, device processing (photolithography), and device performance characterization of these NVM materials. Material characterization methods like SEM and TEM (material microstructure and morphology), spectroscopic ellipsometry, x-ray diffraction, atomic force microscopy, photoluminescence, temperature-dependent Hall-effect/sheet-resistivity, temperature-dependent current-voltage, deep level transient spectroscopy, transmission line, TDTR (Time Domain Thermo Reflectance) can be applicable to establish structure property relationships. The impact of this research effort will enable significant improvement in autonomous capabilities of Air Force weapons systems and improve the decision making process under the big data challenges.

 

key words

Switching memory devices; memristors; artificial intelligence; machine learning; Thermal Management; Reliability; change in state; in-situ biasing

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral and Senior applicants