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MASS SPECTROMETRY TO IDENTIFY PREDICTIVE FAILURE WITH CHEMICAL DETECTION IN MICROELECTRONIC SYSTEMS

thesis
posted on 07.12.2020, 18:11 by Hersh Rai

The world of technology continues to grow each moment and is embraced in all parts of society. The United States Department of Defense has integrated powerful computing technology and systems to bolster its mission of defending the nation. However, computing technology is susceptible to failure from being overworked in certain environments and operational settings. This thesis seeks to identify the feasibility of fault detection and diagnostics from the basis of chemical signature analysis of microelectronics within a system.


Multiple studies relating to fault detection methods exist as fault detection is employed to monitor and maintain the status of an electronic system. The DoD at large employs multiple fault detection methods in order to maintain operational integrity and availability of electronic systems. The study focuses on chemical detection methods as a means of fault detection and diagnosis within electronic systems. The chemical detection method utilizes Atmospheric Pressure Chemical Ionization with Mass Spectrometry to perform bulk sampling procedures in order to create mass-to-charge spectra for interpretation. Rather, this method focuses on non-invasive and pre-emptive failure detection in electronic systems.


Each microelectronic within a computing system has a unique chemical composition, and thus unique bulk chemical signature produced upon failure. The study focuses on Raspberry Pis to test chemical signature output at the power supply in an operational and failure state. By analyzing the signature, predictive failure can be identified with the evolution of Oxalic acid prior and after power supply failure. In conjunction, the tandem mass spectrometry results collected display the evolution of Oxalic acid as carbon dioxide and water molecules are lost resulting in decarboxylation and dehydration of the Raspberry Pi. Through this research, the DoD can better equip the warfighter with predictive fault detection capabilities using chemical detection. In addition, costs and time spent on power, space, and weight applications will be reduced. Overall, operational readiness and superiority will be bolstered in the warfighting environment.

History

Degree Type

Master of Science

Department

Computer and Information Technology

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

J. Eric Dietz

Additional Committee Member 2

Peter Bermel

Additional Committee Member 3

Lonnie Bentley

Additional Committee Member 4

Graham Cooks

Additional Committee Member 5

John Springer