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- Re-Birth - Art Installation by Sheila Pree Bright12:00 am
- Hostile Terrain 94 - InstallationAll day
- "Who Is My Neighbor?" Art by John August Swanson6:00 am
- SHS Flu Clinic9:00 am
- ECE Prospectus Defense: Minxu Peng10:00 am
- As, Not For: Dethroning Our Absolutes11:00 am
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- Discuss the Election - a Facilitated Discussion3:30 pm
- : A Semiparametric Approach to the Detection of Change-points in Volatility Dynamics of Financial Data (Huaiyu Hu - Boston University)4:00 pm
- Mind, Body, Spirit Yoga5:30 pm
- Panel Event: Domestic Violence during COVID-196:00 pm
- Discuss the Election - A Discussion with Students and Staff6:00 pm
- Student Life in Israel with Professor Mira Angrist6:00 pm
ECE Prospectus Defense: Minxu Peng
Title: Probablistic Particle Beam Microscopy
Advisor: Professor Vivek Goyal, ECE
Chair: David Castanon, (ECE SE)
Committee: Professor Lei Tian, ECE; Professor Karl Berggren, Electrical Engineering and Computer Science (EECS) in MIT
Abstract: Particle beam microscopes have enabled researchers to understand material properties by imaging a specimen at near atomic resolution. In a particle beam microscope, a beam of particles is incident on and interacts with a sample, producing secondary electrons which reflect sample topography information. The generated secondary electrons are then accelerated towards a detector and produce images of the sample surface. One limiting factor of these microscopes is the sample damage induced by the particle beam, especially for delicate biological samples. Standard techniques for sample damage reduction generally involve source dose reduction, in the form of reduced dwell time or reduced beam current. As a consequence, the image quality tends to be reduced as well. In this work, we develop several techniques to improve image quality without changing the dose or to reduce the average dose without loss in image quality. These results are based on exploiting a detailed probabilistic model for particle beam microscope measurements at the level of secondary electron counts. We demonstrate that repeated low-dose measurements are more informative than a single measurement with the same total dose through theoretical performance bound analyses and experimental results. We will then present an adaptive dose reallocation strategy to lower required dose compared to the conventional scanning pattern. We will incorporate image priors to further enhance image quality. Finally, we intend to show our proposed method is also useful in edge detection, which is of great use in assessing semiconductor manufacturing accuracy.
When | 10:00 am to 12:00 pm on Thursday, November 5, 2020 |
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Location | https://us02web.zoom.us/j/87236596704?pwd=SmlCTzg4Q0Fqa09wMGNXaUhFanlidz09#success |