The key objective of nanomaterial metrology is to extract relevant information on nano-structure for quantitatively correlating structure-property with functionality. Historic improvements on in- strumentation platforms has enabled comprehensive capture of the information stream both glob- ally and locally. For example, the impressive scanning transmission electron microscopy (STEM) progress is the access to vibrational spectroscopic signals such as atomically resolved electron en- ergy loss spectroscopy (EELS) and the most recent ptychography. This is particularly pertinent in the scanning probe microscopy (SPM) community that has seen a rapidly growing trend towards simultaneous capture of multiple imaging channel and increasing data sizes. Meanwhile signal pro- cessing analysis remained in the same, depending on simple physics models. This approach by definition ignores the material behaviors associated with the deviations from simple physics models and hence require more complex dynamic models. Introduction of such models, in turn, can lead to spurious growth of free parameters and potential overfitting etc. To derive signal analysis pathways necessitated by large,complex datasets generated by progress in instrumentation hardware, here we propose data-physics inference driven approaches for high- veracity and information-rich nanomaterial metrology. Mathematically, we found structural spar- sity regularizations extremely useful which are explained at corresponding applications in later chapters. In a nutshell, we overview the following contributions: 1.We proposed a physics-infused semi-parametric regression approach for estimating the size distribution of nanoparticles with DLS measurements, yielding more details of the size distribution than the traditional methodology. Our methodology expands DLS capability of characterizing heterogeneously shaped nanoparticles. 2.We proposed a two-level structural sparsity regularized regression model and correspondingly developed a variant of group orthogonal matching pursuit algorithm for simultaneously estimating global periodic structure and detecting local outlier structures in noisy STEM images. We believe this an important step toward automatic phase. 3.We develop and implement a universal real-time image reconstruction algorithm from rapid and sparse STEM scans for non-invasive and high-dynamic range imaging. We build up and opensource the systematic platform that fundamentally push the evolution of STEM for both imaging and e-beam based atom-by-atom fabrication, forming a marriage between the imaging and manipulation modes via intelligent and adaptive responses to the real-time material evolution. / A Dissertation submitted to the Department of Industrial and Manufacturing Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Summer Semester 2018. / July 6, 2018. / Includes bibliographical references. / Chiwoo Park, Professor Directing Dissertation; Anuj Srivastava, University Representative; Zhiyong Liang, Committee Member; Arda Vanli, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_655883 |
| Contributors | Li, Xin (author), Park, Chiwoo (professor directing dissertation), Srivastava, Anuj, 1968- (university representative), Liang, Zhiyong (committee member), Vanli, Omer Arda (committee member), Florida State University (degree granting institution), College of Engineering (degree granting college), Department of Industrial and Manufacturing Engineering (degree granting departmentdgg) |
| Publisher | Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, doctoral thesis |
| Format | 1 online resource (110 pages), computer, application/pdf |
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