Abstract
The metal-semiconductor contact resistivity has started to play a critical role for the overall device performance as Si is reaching 10-nm size ranges. The International Technology Roadmap for Semiconductors (ITRS) target predicts a requirement of 10-9; Ω·cm2 by 2023 which has been a challenging target to achieve. This paper explores the impact of doping concentration, Schottky barrier height, strain, and SiGe mole fraction on the resistivity of Si/SiGe p-type metal-oxide semiconductor (PMOS) contacts with 20-band atomistic tight binding quantum transport simulations. Commonly used simple effective mass approximation models are shown to overestimate the resistivity values. The predicted model results are compared with experimental data and the device parameters needed to achieve 10-9; Ω·cm2 are identified.
Original language | English |
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Article number | 8365831 |
Pages (from-to) | 968-973 |
Number of pages | 6 |
Journal | IEEE Transactions on Nanotechnology |
Volume | 17 |
Issue number | 5 |
DOIs | |
Publication status | Published - 2018 Sept |
Bibliographical note
Funding Information:Manuscript received December 15, 2017; revised May 14, 2018; accepted May 22, 2018. Date of publication May 25, 2018; date of current version September 6, 2018. This work was supported in part by the Intel Corporation and in part by the Accelerating Nanoscale Transistor Innovation with NEMO5 on Blue Waters PRAC allocation support by the National Science Foundation (Award OCI-0832623). This work was supported in part by funding from the Semiconductor Research Corporations Global Research Collaboration (GRC) (2653.001). The review of this paper was arranged by the IEEE NANO 2017 Guest Editors. (Corresponding author: Prasad Sarangapani.) P. Sarangapani, M. Povolotskyi, G. Klimeck, and T. Kubis are with the Network for Computational Nanotechnology, Purdue University, West Lafayette, IN 47907 USA (e-mail:, psaranga@purdue.edu; mpovolot@purdue.edu; gekco@ purdue.edu; tkubis@purdue.edu).
Publisher Copyright:
© 2002-2012 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Electrical and Electronic Engineering