• There are no events for this day.

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
Location https://us02web.zoom.us/j/87236596704?pwd=SmlCTzg4Q0Fqa09wMGNXaUhFanlidz09#success