4.7 Article

Scaling limits of monolayer AlN and GaN MOSFETs

期刊

APPLIED SURFACE SCIENCE
卷 634, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.apsusc.2023.157613

关键词

ML AlN and GaN; MOSFET; Scale limit; 1-nm-L-g; Quantum transport simulation

向作者/读者索取更多资源

In this study, monolayer AlN and GaN metal-oxidesemiconductor field-effect transistors (MOSFETs) were simulated using ab initio quantum transport simulation. It was found that the n-type ML AlN (GaN) MOSFETs outperform their p-type counterparts and can exceed the 2028 IRDS low-power and high-performance targets at gate lengths of 3nm and 2nm, respectively. The optimal n-type ML AlN (GaN) MOSFETs exhibit the highest (second-highest) I-on among all studied n-type ML MOSFETs and push the gate length limit beyond the 2013 ITRS targets, reaching 2nm and 1nm for low-power and high-performance, respectively.
The shrinking of transistors to ultra-scale limits is in great demand to extend Moore's law, where the search for proper alternative channel materials is significant. The III-V group compounds are regarded as post-Si candidates for their high electron mobility. Herein, we simulate the monolayer (ML) AlN and GaN metal-oxidesemiconductor field-effect transistors (MOSFETs) with ab initio quantum transport simulation to evaluate their scale limit. The ML AlN and GaN MOSFETs exhibit much better n-type performances than their p-type counterparts, and the n-type ML AlN (GaN) MOSFETs can surpass the International Roadmap for Device and Systems (IRDS, 2022 version) lower-power (LP) and high-performance (HP) target for the year 2028 even at gate length (L-g) of 3 and 2 nm, respectively. Encouragingly, the optimal n-type ML AlN (GaN) MOSFETs possess the highest (second-highest) I-on against all studied n-type ML MOSFETs and propel the L-g limit that outperforms the International Technology Roadmap for Semiconductors (ITRS, 2013 version) LP and HP targets to 2 and 1 (2) nm, respectively.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据