Fu, Zih YingZih YingFuChein, Wei HsinWei HsinCheinYang, Fu ShengFu ShengYangLIANG-CHIA CHEN2023-07-242023-07-242023-01-0197815106609910277786Xhttps://scholars.lib.ntu.edu.tw/handle/123456789/634131A neural network-assisted spectral scatterometry method is presented to measure multi-dimensional critical dimensions (CDs) on high aspect ratio (HAR) structures with micron or submicron scales. With the rise of 3D integrated circuit packaging, there is a need for accurate characterization of HAR sub-micron structures. This method uses DUV scatterometry and a broadband light source from DUV to visible light to gather multi-channel reflection data. The inverse modeling method and artificial neural network model enable accurate measurement of multiple CDs of test structures. The results showed accurate measurement of deep trench critical dimensions with a nominal line width of 0.6 μm and aspect ratio up to 5:1, with accuracy within a few nanometers comparable to SEM results using the same sample.artificial neural network (ANN) | deep ultraviolet (DUV) | finite-difference time-domain (FDTD) | high aspect ratio (HAR) | Optical critical dimension (OCD) | spectral scatterometryconference paper10.1117/12.26576422-s2.0-85163364972https://api.elsevier.com/content/abstract/scopus_id/85163364972