About This Webinar
The signal-to-noise ratio (SNR) in imaging plays a crucial role, particularly in hyperspectral imaging where filters substantially reduce the input signal. Not all SWIR imagers are created equal, as different detection applications necessitate varying sensor capabilities. Various factors, including cost points, array size and underlying materials correspond to different levels of functionality. The selection of a SWIR camera assumes an important role in shaping detection and imaging outcomes, particularly when confronted with low signals, high frame rates, or spectral imaging.
This talk provides a comprehensive examination of dark current, read noise, quantum efficiency, and frame rate, shedding light on their significant impact of SWIR phenomena detection in both imaging and spectroscopic applications. Throughout this talk, Ettenberg details how these various factors interact and affect one another. While the SWIR wavelength range holds undeniable significance in discerning specific material characteristics, it is imperative to recognize that SNR can be the determining factor enabling error-free execution of detection tasks by machine vision algorithms. A SWIR imager can be the right imager for many applications and investing in the appropriate SWIR imager is crucial, as selecting an unsuitable sensor choice may yield poor, inconsistent and/or incorrect results.
*** This presentation premiered during the
2024 Vision Spectra Conference. For more information on Photonics Media conferences and summits, visit
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About the presenter
Martin H. Ettenberg, Ph.D., is currently CEO, board member, and founder of Princeton Infrared Technologies, Inc., a company dedicated to commercialization of NIR/SWIR detection technologies. They have been in business for 10 years and have several award-winning products. Ettenberg has been developing InGaAs detectors for over 25 years starting at Sensors Unlimited Inc./Goodrich Corporation. He led the development of the first night-vision-capable InGaAs camera, which was later commercialized as the SU320MX. In addition to fabricating larger focal plane arrays and more sensitive cameras, he was instrumental in developing smaller cameras for robotics and unmanned aerial vehicles. His DARPA work led to his nomination in 2005 for the DARPA Significant Achievement Award. Ettenberg graduated with a master’s and a doctorate degree from the University of Virginia, Department of Materials Science and Engineering. He also received his bachelor’s degree in materials science and engineering from Cornell University.