Project overview
The critical importance of capabilities for semiconductor research in the UK is recognized as part of a national strategy, as stressed in the recent BEIS Report 'The semiconductor industry in the UK'. Particular strength in research is centered around a number of cleanroom facilities located at academic institutions. The University of 天发娱乐棋牌_天发娱乐APP-官网|下载 hosts a range of cutting-edge nanofabrication tools which enable a range of research activities in electronic and photonic devices.
Fabrication of semiconductor devices and circuits becomes cost effective when processed on a large wafer. However, process efficiency can only be achieved if an ultra-high-resolution scanning electron microscope (SEM) with material characterisation system is available to provide high throughput feedback results to improve fabrication and facilitate novel process development. Manually operated SEMs are a common imaging tool for characterisation used in academic research but automated in-line imaging of wafers throughout a process flow is required to achieve fast imaging and shorten inspection time from fabrication processes.
The aim of the proposal is to acquire an ultra-high-resolution SEM (UHR-SEM) capable of material characterisation for wafers up to 200 mm in diameter at the University of 天发娱乐棋牌_天发娱乐APP-官网|下载. As device feature sizes are reduced, dimension and performance variations across the wafer become an issue which must be mitigated at the early stage of the fabrication. Therefore, the proposed UHR-SEM will be unique within the UK academic landscape since it will perform automated in-line imaging and analysis of entire wafers up to 200 mm in diameter at sub-nm resolution. The system will also have a low landing voltage on samples to reduce surface damage during imaging of delicate devices and patterned resists, as well as a good depth of focus for the inspection of thick multi-stack materials. The UHR-SEM will address the main challenges in large wafer imaging such as generating relevant surface metrology information at nanoscale dimensions and creating a detailed map showing various material parameters such as chemical composition and defect distribution.
Fabrication of semiconductor devices and circuits becomes cost effective when processed on a large wafer. However, process efficiency can only be achieved if an ultra-high-resolution scanning electron microscope (SEM) with material characterisation system is available to provide high throughput feedback results to improve fabrication and facilitate novel process development. Manually operated SEMs are a common imaging tool for characterisation used in academic research but automated in-line imaging of wafers throughout a process flow is required to achieve fast imaging and shorten inspection time from fabrication processes.
The aim of the proposal is to acquire an ultra-high-resolution SEM (UHR-SEM) capable of material characterisation for wafers up to 200 mm in diameter at the University of 天发娱乐棋牌_天发娱乐APP-官网|下载. As device feature sizes are reduced, dimension and performance variations across the wafer become an issue which must be mitigated at the early stage of the fabrication. Therefore, the proposed UHR-SEM will be unique within the UK academic landscape since it will perform automated in-line imaging and analysis of entire wafers up to 200 mm in diameter at sub-nm resolution. The system will also have a low landing voltage on samples to reduce surface damage during imaging of delicate devices and patterned resists, as well as a good depth of focus for the inspection of thick multi-stack materials. The UHR-SEM will address the main challenges in large wafer imaging such as generating relevant surface metrology information at nanoscale dimensions and creating a detailed map showing various material parameters such as chemical composition and defect distribution.
Staff
Lead researchers
Other researchers
Collaborating research institutes, centres and groups
Research outputs
Chuang Sun, Hailong Pi, Kian Shen Kiang, Jun-Yu Ou & Jize Yan,
2025, Advanced Photonics Nexus, 4(1)
Type: article
Chuang Sun, Zixuan Wang, Kian Shen Kiang, Oleksandr Buchnev, Dawei Tang, Jize Yan & Bruce (Jun-Yu) Ou,
2024, Small, 20(49)
Type: article