CAREER: Entropy Oxide Memristors for Software-equivalent Neuromorphic Computing

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Training of artificial intelligence (AI) is foreseen to consume exponential amounts of computing resources in the next decade, at large environmental and financial costs. Memristive technology has shown significant potential to be part of high-density next generation neuromorphic processors to support energy efficient AI algorithms. The physics of memristive switching is promising since in theory, the device state can change through the movement of only few ions under very low voltage. However, device non-idealities have prevented using memristor chips for neural network training due to performance sub-par to software solutions. Memristors and other emerging devices promise major societal impact via key applications e.g. medical implants, robotics, Internet of Things, etc. that need compact and efficient computing. However, their commercial adoption requires academic leaps in performance, yield, reliability and workforce training. This project will develop the next generation of memristors, based on entropy-stabilized oxides with engineered switching dynamics. This work will enable the discovery of new materials for memristor devices and support the development of efficient computing technologies, in line with the national focus on semiconductor competitiveness and next generation microelectronics. Moreover, this project seamlessly integrates the research and education objectives to support the necessary workforce development in this field. The efforts include overhaul of a nanoelectronics course to make it more accessible, interdisciplinary research training for diverse students, the development of a comprehensive virtual reality memristor simulator, providing github documentation to the community and outreach to local high schools. The developed models and experimental platforms will give students a hands-on education on these devices, their manufacturing, and their use in neuromorphic hardware for novel applications.This CAREER project aims to create ultra-low variability analog memristors by exploiting the underlying material properties and physical phenomena in entropy-stabilized oxides. By comparison with existing approaches attempting structural filament confinement at mesoscopic (micron) scales using phase separation, this project proposes filament confinement at nanometer scales for better switching uniformity. Entropy-stabilized oxides will be explored because they provide a broad compositional space and potential for high-quality films deposited at CMOS-suitable temperatures where the requirements for desired switching dynamics can be met. These complex oxides will be used to investigate the nanoscale control of the filament dynamics by allowing the oxygen vacancies to preferentially move along energetically-favorable trajectories, while the lattice is entropy-stabilized. To this effect, the work will innovate across four intertwined research and educational objectives. Objective #1 will focus on the identification of suitable entropy-stabilized films and their use in engineering memristor devices with ultra-low variability. Objective #2 will integrate and characterize these entropy-stabilized memristors on transistor chips using suitable buffer layers. Objective #3 will use the experimental dataset to develop multi-dimensional device models useful in neural network simulations and use the fabricated chips to prototype perceptrons and transformer neural networks with software-equivalent accuracy. Objective #4 will target the development and incorporation of experiential learning activities to support the training of diverse students in the field. This project will make critical advances in the understanding of the underlying material and physical requirements for ultra-low variability memristors and investigate their potential performance for next generation artificial intelligence hardware.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.