An Introduction to EUV Light Sources

ASML’s EUV scanners are installed at customer factories and have begun high volume manufacturing (HVM) of high-end semiconductor devices. The latest generation of EUV sources, developed at Cymer in San Diego, operates at 250W of EUV power, while maintaining stringent control of energy stability and dose control, with improved availability and a design for serviceability concept. In this talk, we provide an overview of tin laser-produced-plasma (LPP) extreme-ultraviolet (EUV) sources at 13.5nm enabling HVM at the N5 node and beyond. The field performance of  sources at 250 watts power including the performance of subsystems such as the Collector and the Droplet Generator will be shown. Progress in the development of key technologies for power scaling towards 500W will be described.

From nanotech to living sensors: unraveling the spin physics of biosensing at the nanoscale

Substantial in vitro and physiological experimental results suggest that similar coherent spin physics might underlie phenomena as varied as the biosensing of magnetic fields in animal navigation and the magnetosensitivity of metabolic reactions related to oxidative stress in cells. If this is correct, organisms might behave, for a short time, as “living quantum sensors” and might be studied and controlled using quantum sensing techniques developed for technological sensors. I will outline our approach towards performing coherent quantum measurements and control on proteins, cells and organisms in order to understand how they interact with their environment, and how physiology is regulated by such interactions. Can coherent spin physics be established – or refuted! – to account for physiologically relevant biosensing phenomena, and be manipulated to technological and therapeutic advantage?

STROBE Research Slices: Magnetic Materials

Imaging Magnetic Materials: Structure and Function. Practical solutions to complex imaging challenges, from table-top to facility scale experiments. Learn more about STROBE research on magnetic materials!

Capturing the First Picture of a Black Hole & Beyond

This talk will present the methods and procedures used to produce the first image of a black hole from the Event Horizon Telescope, as well as future developments. It had been theorized for decades that a black hole would leave a “shadow” on a background of hot gas. Taking a picture of this black hole shadow would help to address a number of important scientific questions, both on the nature of black holes and the validity of general relativity. Unfortunately, due to its small size, traditional imaging approaches require an Earth-sized radio telescope. In this talk, I discuss techniques the Event Horizon Telescope Collaboration has developed to photograph a black hole using the Event Horizon Telescope, a network of telescopes scattered across the globe. Imaging a black holeʼs structure with this computational telescope required us to reconstruct images from sparse measurements, heavily corrupted by atmospheric error. This talk will summarize how the data from the 2017 observations were calibrated and imaged, and explain some of the challenges that arise with a heterogeneous telescope array like the EHT. The talk will also discuss how we are developing machine learning methods to help design future telescope arrays.

Katie Bouman is an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. Before joining Caltech, she was a postdoctoral fellow in the Harvard-Smithsonian Center for Astrophysics. She received her Ph.D. in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT in EECS. Before coming to MIT, she received her bachelor’s degree in Electrical Engineering from the University of Michigan. The focus of her research is on using emerging computational methods to push the boundaries of interdisciplinary imaging.

Towards Automated Information Extraction from High Resolution Transmission Electron Microscopy Images

Transmission electron microscopy (TEM) is the characterization method of choice to observe the atomic-scale and microstructural local features within materials that play a critical role in material performance. However, a bottleneck exists between image acquisition and the extraction of relevant information that can be used in a materials design feedback loop. While image analysis of individual images can easily identify regions of interest and determine whether they contain defects, it is prohibitively time-consuming to manually perform this analysis on large numbers of images. Advances in machine learning and computer vision have made high accuracy automated image interpretation possible. Here, we present application of machine learning and other high-throughput methods to TEM images for nanoparticle identification and microstructural characterization.

The Challenges of Mastering Professional and Geographic Distance

Many important problems are attacked by putting together teams that span both professional distance and are geographically dispersed.

I have spent the better part of three decades studying such teams, identifying both the challenges involved and ways of meeting these challenges. I will review these, with illustrations from scientific project teams.

Electron and Photon Detection for Microscopies

Seeing small things takes bright lights and great optics. But you still have to see something. This talk will discuss detectors for electron and x-ray microscopies: how they work, what are the challenges and where are the opportunities. The competition is intense: the human eye has ~108  ‘pixels’ and a dynamic range of ~104  and has a direct connection to a built-in neural processor). No camera today can match these specs (although we are getting close). The use of silicon as a sensing medium, together with the dramatic advances in microelectronics (“Moore’s law”) has transformed how we record images. Is detection a solved problem?

Bluesky Project at DOE Light Sources

Please join us for a seminar by the Bluesky project at DOE light sources. Bluesky provides a python-based framework to handle experimental data through the different parts of the data flow process. The approach is modular, and can provide a good starting point for discussions in STROBE about data management. In particular, we will discuss this topic at the annual retreat.

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