Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.652070
Title: Diagnostic and statistical modelling techniques applied to an aluminium plasma etch process
Author: Hannah, James Robertson
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1992
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Abstract:
Dry etching processes are crucial to the fabrication of VLSI and ULSI circuits and are likely to continue so for the foreseeable future. However, the technique remains difficult to implement and is not well understood. To a great extent, this is a result of the use of novel machine configurations and chemical recipes which are employed far in advance of any real understanding of the interactions occurring within the process chamber. Historically, process development in this field has proceeded as a result of improvements in vacuum technology coupled to empirical one-dimensional trend analyses. These traditional approaches to process development have become severely limited with the advent of ULSI. This project addressed the requirement for improved process characterisation by investigating diagnostic techniques and rigorous statistical modelling as applied to one of the most challenging leading edge processes: the BCl3/Cl2/He dry etching of aluminium alloys. Optical emission spectroscopy, electrostatic Langmuir probes and mass spectrometry were used to investigate the dry etch behaviour. During this research, a novel in-situ SIMS implementation of the mass spectrometer was used to investigate plasma generated positive ions. Details of detected ions and intensities during etching are presented. A CCF experimental design was formulated from the major RIE machine parameters of rf power, system pressure and % Cl2/BCl3. The final quadratic model covered 3 manipulated machine variables, 3 process parameters and 6 performance parameters. This is the first time that rigorous statistical modelling techniques have been applied to the BCl3/Cl2He based process or to an aluminium dry etch process in order to systematically characterise both the process and performance behaviour. This work presents these models, along with an estimate of each model's accuracy.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.652070  DOI: Not available
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