Electrical & Computer Engineering, Department of


First Advisor

Sina Balkir

Second Advisor

Michael Hoffman

Date of this Version

Summer 7-26-2018

Document Type



S. Murray, CMOS Radioactive Isotope Identification with Multichannel Analyzer and Embedded Neural Network. Master's thesis, Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, 2018.


A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science. Major: Electrical Engineering. Under the Supervision of Professors Sina Balkir and Michael Hoffman. Lincoln, Nebraska: August, 2018

Copyright (c) 2018 Samuel Murray


A radiation detection and identification system is designed and implemented to perform gamma ray spectroscopy on radioactive sources and identify which isotopes are present in the sources. A multichannel analyzer is implemented on an ASIC to process the signal produced from gamma rays detected by a scintillator and photomultiplier tube and to quantize the gamma ray energies to build a histogram. A fast, low memory embedded neural network is implemented on a microcontroller ASIC to identify the isotopes present in the gamma ray histogram produced by the multichannel analyzer in real time.

Advisors: Sina Balkir and Michael Hoffman