材料科学
半导体
缩放比例
纳米技术
相(物质)
金属
光电子学
化学
冶金
几何学
数学
有机化学
作者
Xiaolong Xu,Shuai Liu,Bo Han,Yimo Han,Kai Yuan,Wanjin Xu,Xiaohan Yao,Pan Li,Shiqi Yang,Wenting Gong,David A. Muller,Peng Gao,Yu Ye,Lun Dai
出处
期刊:Nano Letters
[American Chemical Society]
日期:2019-09-03
卷期号:19 (10): 6845-6852
被引量:61
标识
DOI:10.1021/acs.nanolett.9b02006
摘要
Two-dimensional (2D) layered semiconductors, with their ultimate atomic thickness, have shown promise to scale down transistors for modern integrated circuitry. However, the electrical contacts that connect these materials with external bulky metals are usually unsatisfactory, which limits the transistor performance. Recently, contacting 2D semiconductors using coplanar 2D conductors has shown promise in reducing the problematic high contact resistance. However, many of these methods are not ideal for scaled production. Here, we report on the large-scale, spatially controlled chemical assembly of the integrated 2H-MoTe2 field-effect transistors (FETs) with coplanar metallic 1T′-MoTe2 contacts via phase engineered approaches. We demonstrate that the heterophase FETs exhibit ohmic contact behavior with low contact resistance, resulting from the coplanar seamless contact between 2H and 1T′-MoTe2 confirmed by transmission electron microscopy characterizations. The average mobility of the heterophase FETs was measured to be as high as 23 cm2 V–1 s–1 (comparable with those of exfoliated single crystals), due to the large 2H-MoTe2 single-crystalline domain size (486 ± 187 μm). By developing a patterned growth method, we realize the 1T′-MoTe2 gated heterophase FET array whose components of the channel, gate, and contacts are all 2D materials. Finally, we transfer the heterophase device array onto a flexible substrate and demonstrate the near-infrared photoresponse with high photoresponsivity (∼1.02 A/W). Our study provides a basis for the large-scale application of phase-engineered coplanar MoTe2 semiconductor–metal structure in advanced electronics and optoelectronics.
科研通智能强力驱动
Strongly Powered by AbleSci AI