HAMAMATSU

Hamamatsu Corporation

Photonics webinar series

Using SNR simulation to select a photodetector

Dino Butron, Applications Engineer III, Hamamatsu Corporation
August 18, 2020

About this webinar

Signal-to-Noise Ratio (SNR) is a statistical figure of merit used to measure the performance of a detector. It is a simple calculation that can compare various detectors to help you choose the right one for your application. Stochastic simulation of detector performance allows for a deeper analysis of the subtle contributions of detector characteristics such as gain, sensitivity, and noise to the SNR in various conditions.

This webinar will review the basics of signal-to-noise ratio calculation, the differences and similarities between different equations for SNR. It will discuss the factors that affect SNR, the sources of noise, and understand how the applications’ conditions determine the key detector characteristics. It will also examine some techniques to reduce noise, and further increase SNR.

Topics of presentation:

  1. Understand the signal-to-noise ratio calculation
  2. Understand how detector and amplifier gain impacts SNR
  3. Learn the factors that affect SNR
  4. Learn how to determine the dominant noise source
  5. Become familiar with using SNR to determine detector selection

About the presenter

Dino Butron

Dino Butron is an Applications Engineer III at Hamamatsu Corporation in Bridgewater, NJ where his focus is on high sensitivity photodetectors for use in various markets. He is currently involved in leading discussions for detector selection and developing simulation tools.

Dino is an expert in the operating principles and application of various detectors such as photodiodes, avalanche photodiodes (APD), SPPC (SPAD), MPPC (SiPM), and photomultiplier tubes (PMT). He has worked on many photodetector experiments resulting in a deep understanding of detector performance. In addition, he has vast knowledge programming signal-to-noise ratio and output simulations. He received his Bachelor’s degree in Electrical Engineering from Manhattan College, Riverdale, NY, in 2012.

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