Rational Design and Characterization of 2D Metal-Semiconductor Junctions for Optoelectronics by Combining Quantum Transport and Excited State Carrier Dynamics Simulations: Case of 2H-WTe2 Based Junctions.
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引用次数: 0
Abstract
Rising 2D metal-semiconductor junctions (MSJs) greatly advance nanoelectronics. Currently, evaluating the performance of MSJs mainly relies on determining the formation of Ohmic or Schottky contacts through static electronic structures. However, when MSJs are integrated into optoelectronic devices, dynamic transport and excited state carrier dynamics become crucial, yet the impact of the interface remains unclear. Herein, taking 2H-WTe2 based vertical and lateral MSJs as examples, their performances are evaluated in the perspective of ground and excited states. Faced with the significant band hybridization induced by strong interfacial coupling, identifying Ohmic contact only by analyzing electronic structures possibly becomes defunct. Through quantum transport simulation, the junction with the largest on-state current can be further filtered. Meanwhile, non-adiabatic molecular dynamics simulation uncovers the carrier lifetime can be shortened or extended as the interfaces are formed, which depends on the competitive relationship between electron-phonon coupling and trap states. In these MSJs studied, the photoexcited carriers with the nanosecond lifetime can be rapidly conducted away by electrodes, indicating a high-effect utilization rate. The work advances the all-around routine for rational designing and characterizing 2D MSJs.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.