LEARNING BEFORE ERRING: THE INFLUENCE OF DIELECTRIC MATERIALS TO PURSUE MOORE’S LAW

Authors

  • William A. Young Ohio University
  • Savas Kaya Ohio University
  • Gary R. Weckman Ohio University

DOI:

https://doi.org/10.23055/ijietap.2009.16.2.73

Keywords:

Moore’s Law, gate leakage current density, high-k dielectric material, low-powered devices

Abstract

The semiconductor industry’s ambitious scaling trends provide important guidelines for development by integrating knowledge from the past to self-correct its course. This paper illustrates this phenomenon by investigating the recent crises of static power losses in transistor structures. The impact of the power leaked through a device’s drain has led to a drastic change in dielectric materials used, where silicon dioxide (SiO2) insulators have been replaced with novel high-k dielectric constant materials. However, quantifiable evidence, which has emerged recently in research, suggests that even these new materials may not be sufficient to continue the scaling trajectory expected of Moore’s Law, especially for low-powered devices. Based on industrial learning achieved from past trends, data collected for new dielectric materials, such as hafnium dioxide (HfO2), and implied device scaling using Moore’s Law, analysis suggests that current stand-by leakage limits will be breached in the near future.

Author Biographies

William A. Young, Ohio University

William Young is a doctoral candidate in the Integrated Engineering program at Ohio University. His dissertation is focused on developing a team-compatibility decision support system. To fund this project, Mr. Young received Ohio University’s 2007 Student Enhancement Award, which promotes creative academic research. Mr. Young recently accepted a position as a NFS GK-12 Fellow for the Science Technology Enrichment of Appalachian Middle-schoolers (STEAM) project. William received his Master’s (MSEE) and Bachelor’s (BSEE) degrees in Electrical Engineering at Ohio in 2005 and 2002 respectively. As a master’s student, Young worked on a preliminary design cost estimation project for General Electric Aircraft Engines and developed a methodology to model a parts’ family learning rate. His primary research is focused on developing methods for decision support systems, which includes intelligent systems such as neural networks.

Savas Kaya, Ohio University

Savas Kaya (M’01) graduated from Istanbul Technical University in 1992 with a BSc in Electronics and Communication Engineering, received M.Phil. Degree in 1994 from the University of Cambridge, U.K., and Ph.D. degree in 1998 from Imperial College of Science, Technology & Medicine, London, U.K., for his work on strained Si quantum wells on vicinal substrates. From 1998 to 2001, he was a Postdoctoral Researcher at the University of Glasgow, Scotland, U.K., carrying out research in transport and scaling in Si/SiGe MOSFETs, and fluctuation phenomena in decanano MOSFETs. He is currently an Associate Professor at the Russ College of Engineering at Ohio University, Athens, OH. He has served as Air Force Office of Scientific Research Summer Faculty Fellow in 2006 and 2007. He published over 30 journal papers and 45 conference proceedings. His other interests include nanoelectronic devices and circuits, TCAD, transport theory, nanostructures, process integration, ionic transport and biomolecular modeling in trans-membrane proteins. Dr. Kaya was a member of the organizing committee for IWCE’7, 2000, and IEEE Nanotech’6, 2006.

Gary R. Weckman, Ohio University

Gary Weckman was a faculty member at Texas A&M University-Kingsville for six years before joining the Ohio University faculty in 2002 as an associate professor in Industrial and Systems Engineering. He has also practiced industrial engineering for over 12 years with firms such as; General Electric Aircraft Engines, Kenner Products and The Trane Company. Dr. Weckman’s primary research focus has been multidisciplinary applications utilizing knowledge extraction techniques with artificial neural networks. He has used ANNs to model complex systems such as large-scale telecommunication network reliability, ecological relationships, stock market behavior, and industrial process scheduling. In addition, his research includes industrial safety and health applications and is on the Advisory Board for the University of Cincinnati NIOSH Occupational Safety and Health Education and Research Center Pilot Research Project.

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Published

2009-08-11

How to Cite

Young, W. A., Kaya, S., & Weckman, G. R. (2009). LEARNING BEFORE ERRING: THE INFLUENCE OF DIELECTRIC MATERIALS TO PURSUE MOORE’S LAW. International Journal of Industrial Engineering: Theory, Applications and Practice, 16(2), 91–98. https://doi.org/10.23055/ijietap.2009.16.2.73

Issue

Vol. 16 No. 2 (2009)

Section

Management of Technology

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