STROBE Seminar

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 they 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).

As a follow-up to Seminar I, I will discuss specific examples of fs UPS and photovoltage experiments on industrially relevant materials and stacks. Aside from single crystal Si wafers, virtually all materials found in MOS devices, photovoltaics, oxides, organic films (OLEDs, resists) and phase change materials, are polycrystalline or amorphous. Angle integrated UPS provides high count rates that increase sensitivity useful for identifying defect state densities in materials.

CU Boulder · JILA X317 | UC Berkeley · Birge 468 Critical to the design and development of present and future semiconductor and quantum devices is the full understanding of the electronic structure of the materials that comprise the complex functional stacks in a non-destructive way. In Seminars I and II, I will describe the application of femtosecond ultraviolet photoelectron and photovoltage spectroscopy (fs UPPS) to fully characterize the electronic structure of industrially important materials and devices.

Abstract:  In-vivo imaging through multimode fibers has been recently accomplished. Multimode fibers are attractive for endoscopic applications due to their thin cross-section, a large number of degrees of freedom, and flexibility. However modal dispersion and intermodal coupling preclude direct image transmission. The development of fast spatial phase control enables focus scanning and structured illumination for different novel imaging modalities. We discuss the implications of these techniques for ultrathin optical endoscopy. 

For Zoom passcode information, please contact Lauren Mason at masonlw@colorado.edu.

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Abstract

Abstract: This tutorial will introduce computational imaging as a broad range of techniques in which algorithms play a major role in the final image formation process. The basic recipe for computational imaging involves coming up with a forward model that can simulate/predict what your imaging system measures. This model is computationally inverted to reconstruct the object under investigation. We will also discuss how to incorporate prior knowledge and constructs from machine learning into the computational reconstruction algorithms to improve the image reconstruction fidelity.

If joining by zoom: 

Meeting ID: 966 4842 0411,  Passcode: STROBE-23