表征(材料科学)
薄脆饼
硅
材料科学
工作流程
半导体
光电子学
吞吐量
电子
纳米技术
工程物理
计算机科学
工程类
物理
核物理学
操作系统
数据库
无线
作者
Libor Strakoš,Ondřej Machek,T. Vystavěl,Andreas Schulze,Han Han,Matty Caymax,Richard Young
出处
期刊:Proceedings
日期:2018-11-01
标识
DOI:10.31399/asm.cp.istfa2018p0363
摘要
Abstract As semiconductor devices continue to shrink, novel materials (e.g. (Si)Ge, III/V) are being tested and incorporated to boost device performance. Such materials are difficult to grow on Si wafers without forming crystalline defects due to lattice mismatch. Such defects can decrease or compromise device performance. For this reason, non-destructive, high throughput and reliable analytical techniques are required. In this paper Electron Channeling Contrast Imaging (ECCI), large area mapping and defect detection using deep learning are combined in an analytical workflow for the characterization of the defectivity of “beyond Silicon” materials. Such a workflow addresses the requirements for large areas 10-4 cm2 with defect density down to 104 cm-2.
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