| About this Abstract |
| Meeting |
MS&T21: Materials Science & Technology
|
| Symposium
|
Research Lightning Talks
|
| Presentation Title |
Using Unsupervised Learning to Understand Thin Film Growth |
| Author(s) |
Kimberly Gliebe, Alp Sehirlioglu |
| On-Site Speaker (Planned) |
Kimberly Gliebe |
| Abstract Scope |
Thin films have a variety of novel applications because of unique dimensional affects as well as their ability to fit in confined spaces such as on-chip devices. A common method to make thin films is pulsed laser deposition, which has a monitoring technique called reflection high energy electron diffraction (RHEED). RHEED is an extremely powerful tool, but it can be difficult to interpret; therefore, unsupervised learning techniques can be utilized to understand the most important features from their data as well as to separate out noise. These techniques will enable a greater understanding of the growth process so that it is easier to grow more novel films. Additionally, unsupervised learning can be applied to other tools such as transmission electron microscopy and x-ray diffraction. |