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Today’s computing systems are reaching fundamental limits with conventional materials like silicon, and with conventional layouts that separate memory and computing. To overcome these challenges, new materials must be explored to enable energy-efficient neuro-inspired devices. Phase-change memory based on chalcogenide materials like Ge2Sb2Te5 hold great promise for both data storage and brain-inspired computing. However, using conventional phase-change materials, memory operation suffers from large power consumption and resistance drift, limiting its potential for these emerging applications.


In this talk, I will present my work on how materials engineering and novel material functionalities can address these challenges and drive unprecedented energy-efficiency in a next-generation memory technology. First, using ultrathin chalcogenide material layers in a superlattice stack on both rigid and flexible platforms, I will demonstrate >10x reduction of both switching energy and resistance drift in phase-change memory (PCM) compared to existing commercial PCM devices. Next, I will present a novel phase-change nanocomposite (Ge4Sb6Te7) enabling bi-directional gradual resistance changes with lowest energy to-date as well as multilevel operation in PCM. I will simultaneously illustrate the fundamental correlations between these chalcogenide material characteristics and improved memory device performances, key to enhance our understanding and ensure robustness of such technology. Finally, I will conclude by outlining my vision and scope for future materials-centric approach bridging between novel materials, advances in their transport physics, and electronic devices for next-generation energy-efficient nanoelectronics.




Asir Intisar Khan is a Ph.D. candidate in Electrical Engineering department at Stanford University, supervised by Professor Eric Pop. He received his M.S. in Electrical Engineering from the same department at Stanford. Prior to joining Stanford in 2018, he received another M.S. (2018) and a B.S. (2016) from Bangladesh University of Engineering and Technology. His research effort and vision encompass exploring novel materials and their functionalities to enable energy-efficient memory, compute devices and interconnects for 3D heterogeneous integration. His research work has enabled lowest-to-date switching current density in phase-change memory technology and has been featured in Forbes Magazine and IEEE Spectrum. He has held Research Intern positions at TSMC and IBM TJ Watson Research Center, USA. Asir is a recipient of the Stanford Graduate Fellowship and 2022 IEEE EDS PhD Student Fellowship.

MSE , NSERL Seminar Room RL3.204

Erik Jonsson School of Engineering and Computer Science


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