Multi-beam Inspection (MBI) development progress and applications

自动光学检测 吞吐量 计算机科学 半导体器件制造 目视检查 极紫外光刻 平版印刷术 薄脆饼 过程(计算) 自动X射线检查 像素 计量学 梁(结构) 可靠性工程 光学 人工智能 电气工程 工程类 电信 物理 图像处理 图像(数学) 操作系统 无线
作者
Eric Long,Weiming Ren,Xinan Luo,Shuo Zhao,Xuerang Hu,Xuedong Liu,Chiyan Kuan,Kevin Chou,Martijn Maassen,Weihua Yin,Aiden Chen,Niladri Sen,Martin Ebert,Lei Liu,Fei Wang,Oliver D. Patterson
标识
DOI:10.1117/12.2553556
摘要

In order to successfully develop and manufacture semiconductor chips, in-line inspection is extremely important. Optical and e-beam inspection are the two major defect inspection approaches used for semiconductor manufacturing. As critical dimensions continue to shrink with each new technology, killer defects are becoming smaller and smaller, reducing the effectiveness of optical inspection, which is resolution limited. A growing number of defect types are just not detectable with optical inspection. A partial solution is to adjust inspection parameters to run "hot", but then the few defects of interest that are captured are buried in large numbers of nuisance defects. E-beam inspection (EBI), in addition to it's unique role of detecting buried defects using voltage contrast (VC), is able to detect these smaller defects, but suffers from throughput constraints. This is because of EBI's substantially smaller pixel size, which takes much longer to tile across the wafer surface, and a lower sampling frequency, because electrons aren't as prevalent as photons. In R&D, this is not as much of a limitation, with EBI commonly deployed as a metric for many physical defects beyond optical inspection resolution as well as lithography related use cases such as process window qualification (PWQ) and EUV print check. However, EBI's adoption during yield ramp and high volume manufacturing (HVM) is limited by these throughput constraints. To address this issue, HMI is developing multi-beam inspection (MBI) systems [1,2]. This latest paper covers three new topics. First, new milestones were achieved in the last year, including simultaneous operation of all beams and defect detection while in this mode, will be reviewed. Second, the importance of minimizing cross-talk between beamlets for MBI and the cross-talk performance of our latest tool is discussed. Finally, simulations of the anticipated throughput gains achievable for a range of physical and voltage contrast inspections for the current system are presented. These throughput gains vary widely and are useful in prioritizing certain inspections over others for practical use, as well as understanding the limiting factors for laggard inspections. Potentially some of these factors can be alleviated. Going forward, the plan is to continue to aggressively increase the number of beamlets while simultaneously further improving the resolution. Overall the HMI MBI program is on track with tool shipments to select customers in the very near future.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
刚刚
刚刚
晴天发布了新的文献求助10
1秒前
jack发布了新的文献求助10
1秒前
昏睡的柜子完成签到,获得积分10
1秒前
666发布了新的文献求助10
1秒前
万能图书馆应助WYN采纳,获得10
2秒前
脑洞疼应助amengptsd采纳,获得10
2秒前
AllRightReserved应助zhangHR采纳,获得10
3秒前
AllRightReserved应助zhangHR采纳,获得10
3秒前
AllRightReserved应助zhangHR采纳,获得10
3秒前
l_v关闭了l_v文献求助
3秒前
xiaofei应助zhangHR采纳,获得10
3秒前
科研通AI6.1应助xiaohe采纳,获得10
3秒前
wubobo发布了新的文献求助10
3秒前
caochuang发布了新的文献求助10
4秒前
4秒前
7秒前
瑞0920发布了新的文献求助10
7秒前
8秒前
9秒前
wubobo完成签到,获得积分10
9秒前
9秒前
慕青应助芬芬采纳,获得10
11秒前
sining发布了新的文献求助10
11秒前
李会计和完成签到,获得积分10
11秒前
11秒前
666完成签到,获得积分10
11秒前
12秒前
蒋宇骁完成签到,获得积分10
12秒前
13秒前
ranj发布了新的文献求助10
13秒前
霜月发布了新的文献求助10
13秒前
14秒前
一路向北发布了新的文献求助10
14秒前
彩色的蓝天完成签到,获得积分10
14秒前
15秒前
16秒前
锡嘻发布了新的文献求助10
16秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
Elevating Next Generation Genomic Science and Technology using Machine Learning in the Healthcare Industry Applied Machine Learning for IoT and Data Analytics 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6443711
求助须知:如何正确求助?哪些是违规求助? 8257506
关于积分的说明 17587476
捐赠科研通 5502428
什么是DOI,文献DOI怎么找? 2900975
邀请新用户注册赠送积分活动 1878057
关于科研通互助平台的介绍 1717534