Despite the long and crucial role of traditional solid-state physics for current silicon-based technologies, next generation neuromorphic, non-volatile memory, and energy devices that are key components in the era of the internet of things (IOT) require novel working principles with quantum physics emerging in low-dimensional materials1-4. The main research direction for the future devices is to realize ‘ultralow device operation energy’, ‘ultrahigh device operation speed’, and ‘large-scale device integration (up to 1015)’, which calls for exploring diverse quantum phenomena in low dimensional device components5,6. In this talk, I will present some of our recent efforts1-7 to establish new device physics for energy intelligent devices, which could be a milestone for the promising future devices. In particular, dynamic convolution neural network, phase transition and other intriguing quantum physics in two-dimensional (2D) materials will be discussed along with logic device, neuromorphic computing, and energy device applications. Reference H. Yang et al. Graphene Barristor, a Triode Device with a Gate-Controlled Schottky Barrier, Science 336, 1140 (2012)S. Cho et al. Phase Patterning for Ohmic Homojunction Contact in MoTe2. Science 349, 625 (2015)D. H. Keum et al. Bandgap opening in few-layered monoclinic MoTe2. Nature Physics 11, 482 (2015)H. Yang et al. Structural and quantum-state phase transition in van der Waals layered materials. Nature Physics 13, 931 (2017)L. Sun et al. Self-selective van der Waals heterostructures for large-scale memory array. Nature Communications 10, 3161 (2019)S. Zheng et al. Resonant tunneling spectroscopy to probe the giant Stark effect in atomically-thin materials. Advanced Materials 32, 1906942 (2020)L. Sun et al. In-sensor Reservoir Computing for Language Learning via Two-dimensional Memristor. Science Advances 7, eabg1455 (2021)
Read full abstract