Title page for ETD etd-154317559631561


Type of Document Master's Thesis
Author Smith, Michael D.
URN etd-154317559631561
Title Estimation of Future Manufacturing Costs for Nanoelectronics Technology
Degree Master of Engineering
Department Industrial and Systems Engineering
Advisory Committee
Advisor Name Title
Ioannou, George
Shewchuk, John P.
Sullivan, William G. Committee Chair
Keywords
  • none
Date of Defense 1996-05-18
Availability unrestricted
Abstract
In this report, a future scenario concerning the economic

direction of the computing industry has been presented. This future

scenario was based on past developments within the computing industry.

The continued miniaturization of semiconductor components was discussed

based on observed trends for transistors. The physical limitations for

transistor devices were also addressed. The use of x-ray lithography for

the construction of devices on a 3nano-scale2 was considered. Next, cost

trends within the microelectronics industry were explored. Although the

cost per transistor has been observed to decrease, total equipment costs

and facilities costs were observed to rise.

Trend extrapolation was next used to predict the future cost per

transistor and the number of transistors per chip. By taking the product

of these two predicted quantities, an equation for the future manufacturing

cost per chip was determined. A parametric cost estimation model (VHSIC

Model) for the prediction of avionics computer system costs was modified

to reflect the future performance parameters of nanoelectronics. Using

data from the x86 design of Intel Microprocessor Chips, undetermined

parameters of the Modified VHSIC Model were calculated. Next, future

performance parameters were used in the model to predict the initial

selling price of future chips. The resulting predictions from this model

indicated that chip prices are expected to increase while the price per

electronic function will decrease. Finally, profit-time models for

semiconductor chips and transistors were derived. These models were used

to predict the future profit for a chip or transistor.

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