Data-driven and probabilistic learning of the process-structure-property relationship in solution-grown tellurene for optimized nanomanufacturing of high-performance nanoelectronics. (March 2019)

• Record Type: Journal Article
• Authors: Wang, Yixiu; de Souza Borges Ferreira, Raquel; Wang, Ruoxing; Qiu, Gang; Li, Gaoda; Qin, Yong; Ye, Peide D.; Sabbaghi, Arman; Wu, Wenzhuo
• Is Part Of: Nano energy. Volume 57(2019)
• Page Start: 480
• Page End: 491
• Publication Date: 2019-03
• Subjects: Tellurene -- Nanomanufacturing -- Data-driven learning -- 2-D materials -- Energy-efficient electronics -- Process-structure-property relationship; Nanoscience -- Periodicals; Nanotechnology -- Periodicals; Nanostructured materials -- Periodicals; Power resources -- Technological innovations -- Periodicals; Nanoscience; Nanostructured materials; Nanotechnology; Power resources -- Technological innovations; Periodicals; 621.042
• DOI: 10.1016/j.nanoen.2018.12.065
• Languages: English
• ISSNs: 2211-2855

Abstract

Abstract: Two-dimensional (2-D) semiconductors have been intensely explored as alternative channel materials for future generation ultra-scaled transistor technology [1–8] . However, significant roadblocks (e.g., poor carrier mobilities [9–11], instability [4, 5, 10], and vague potential in scaling-up [10, 12–15]) exist that prevent the realization of the current state-of-the-art 2-D materials' potential for energy-efficient electronics. The emergent solution-grown tellurene exhibits attractive attributes, e.g., high room-temperature mobility, large on-state current density, air-stability, and tunable material properties through a low-cost, scalable process, to tackle these challenges [16]. Nevertheless, the fundamental manufacturing science of the hydrothermal processing for tellurene remains elusive. Here, we report on the first systematic, data-driven learning of the process-structure-property relationship in solution-grown tellurene, revealing the process factors' effects on tellurene's production yield, dimensions, and transistor-relevant properties, through a holistic approach integrating both the experimental explorations and data analytics. We further demonstrate the application of such fundamental knowledge for developing tellurene transistors with optimized and reliable performance, which can enable the cost-effective realization of high-speed, energy-efficient electronics. Graphical abstract: The fundamental solution-growth mechanism for 2-D tellurene has been revealed through a sophisticated data-driven, probabilistic learning framework for nanomanufacturing high performance nanoelectronics. fx1 Highlights: The growth mechanism for tellurene was learnt via a data-driven framework. A learning-optimization cycle was demonstrated for nanomanufacturing tellurene. The learnt knowledge was used to develop high-performance tellurene transistors.