.png){kind=icon}
返回
Using machine learning with optical profilometry for GaN wafer screening
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-023-29107-9.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
To improve the manufacturing process of GaN wafers, inexpensive wafer screening techniques are required to both provide feedback to the manufacturing process and prevent fabrication on low quality or defective wafers, thus reducing costs resulting from wasted processing effort. Many of the wafer scale characterization techniques-including optical profilometry-produce difficult to interpret results, while models using classical programming techniques require laborious translation of the human-generated data interpretation methodology. Alternatively, machine learning techniques are effective at producing such models if sufficient data is available. For this research project, we fabricated over 6000 vertical PiN GaN diodes across 10 wafers. Using low resolution wafer scale optical profilometry data taken before fabrication, we successfully trained four different machine learning models. All models predict device pass and fail with 70-75% accuracy, and the wafer yield can be predicted within 15% error on the majority of wafers.
Keyword:
VOLTAGE
MODEL
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
27.9W
被引数:
83.5W
机构
引用论文
Improvement of TCAD Augmented Machine Learning Using Autoencoder for Semiconductor Variation Identification and Inverse Design使用自动编码器改进TCAD增强机器学习以进行半导体变化识别和逆向设计.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏分享收藏分享收藏Mixed potential measurements in the elucidation of corrosion mechanisms— 1. Introductory theory
PREAI
分享收藏
查看更多内容