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COORDINATING STRATEGIC CAPACITY PLANNING IN THE SEMICONDUCTOR INDUSTRY SULEYMAN KARABUK and S. DAVID WU
Technical Report 99T-11, Department of IMSE, Lehigh University
COORDINATING STRATEGIC CAPACITY PLANNING
IN THE SEMICONDUCTOR INDUSTRY

SULEYMAN KARABUK and S. DAVID WU
Manufacturing Logistics Institute, Department of Industrial and Manufacturing Systems
Engineering, Lehigh University, Bethlehem, Pennsylvania, david.wu@lehigh.edu

Abstract
We study strategic capacity planning in the semiconductor industry. Working with a major US
semiconductor manufacturer on the strategic configuration of their worldwide production
capacities, we identify two unique characteristics of this problem as follows: (1) wafer demands
and manufacturing capacity are both main sources of uncertainty, and (2) capacity planning must
consider two distinct viewpoints: a product perspective concerning marketing and strategic
demand management, and a process standpoint involving manufacturing, yield, and technology
configuration. These two unique characteristics change, in a fundamental way, how strategic
capacity planning problem should be approached. To describe this complex problem, we first
formulate a multi-stage stochastic program with recourses where demand and capacity
uncertainties are incorporated via a scenario structure. To reconcile the marketing and
manufacturing perspectives to the problem, we consider a decomposition of the planning
problem resembling decentralized decision-making involving the headquarter, the marketing
manager, and the manufacturing manager. To study various trade-offs under this decentralized
structure, we develop recourse approximation schemes simulating different decentralization
strategies. These schemes vary in information requirements and complexity, while providing
insight on the value of information in this environment. We conduct extensive experiments to
analyze the characteristics of decisions under different levels of uncertainties, and assess the
value of alternative schemes from the standpoint of computational requirements and solution
quality. The results indicate that it is possible to arrive at near optimal solutions (within 6.5%)
with information decentralization while using a fraction (less than 16.2%) of the computer time.

Subject Classification: Facilities/equipment planning, capacity expansion: strategic capacity
planning, Programming/stochastic: scenario analysis, recourse approximation, Production,
planning: decentralized coordination. 2
Production capacity is the most significant portion of capital investment in semiconductor wafer
manufacturing. Effective utilization and expansion of production capacity have significant cost
implications, and arguably drives the profitability of the operation. Capacity management in the
industry typically entails long-term strategic planning and short-term operational planning
organized in a hierarchical manner. Strategic planning decisions include how much of which
aggregate microelectronics technology to produce in what facilities, and which capacity element
to expand within what timeframe so as to meet the projected demands. Operational planning
determines capacity adjustment or reconfigurations among microelectronic technologies when
more accurate demand and capacity information becomes available. Operational planning is
frequent and dynamic so as to accommodate weekly production wafer "starts" to be released to
manufacturing. In this paper we focus on the strategic capacity-planning problem while
considering operational planning decisions as the short-term recourse of the capacity plan. Our
study is based on real planning scenarios at a major US semiconductor manufacturer.

One important characteristic of semiconductor capacity planning is that both product demands
and manufacturing capacity are sources of uncertainty. As is the case in most hi-tech industries,
the semiconductor market has a demand structure that is intrinsically volatile. A microelectronic
chip that faces high demands today may be quickly outdated in a few months with the
introduction of a next-generation chip requiring an enhanced manufacturing process. New
manufacturing processes create high variability in the yields, and consequently uncertainty on
the manufacturing throughput, which in turn lead to uncertainty in capacity estimation. Since the
production volumes are typically high (for the interests of achieving economies of scale),
extreme outcomes on demand and capacity realizations can lead to very undesirable business
consequences. Therefore, it is important to consider different scenarios during long-term
capacity planning. We propose a scenario based stochastic programming model to the problem,
which produces a capacity configuration that hedges against extreme outcomes of demand and
capacity fluctuation.


1. BACKGROUND
1.1 Context of the Semiconductor Industry

While capacity configuration and allocation are important decisions for any
manufacturing company, a few factors make this problem especially crucial to the semiconductor
industry. First is the high cost and long lead-time for equipment procurement and clean-room
construction. The semiconductor wafer fabrication process requires state-of-the-art
manufacturing equipment, many costs millions and must be ordered up to twelve months in
advance. Wafers must be made in high purity clean rooms which cost anywhere from several
hundred millions to a few billions to build and take one to two years to construct. Because of the
long lead-time involved, capacity expansion decisions must be made far in advance. A wrong
decision in either over- or under- estimation could have major impacts on profitability: suppose a
decision is made to expand capacity for a certain technology but the demand does not
materialize, significant loss could result due to under-utilization. On the other hand, if the
capacity for a certain technology is not expanded timely to meet market demands, a significant
loss of market share may result. Some believes that the semiconductor stock fluctuations in the
earlier part of year 2000 are triggered by a combination of the above. 3
A second factor that exacerbates the impact of capacity planning in semiconductor is the
rapid advancement of fab technologies and the pace of transition from old technologies to new.
Semiconductor technologies can be defined in several ways, one of which is the space between
features on a semiconductor die, known in the industry as line width. The most expensive and
crucial pieces of equipment in the wafer fabrication line are used in the photolithography
process, where the chip features are defined on a silicon wafer. With each advancement in
photolithography technology, new and more expensive equipment must be purchased so that
features with smaller line widths can be produced. Although the equipment is more expensive, it
allows the manufacturer to either make smaller chips or fit more features on the same size chip,
effectively reducing manufacturing costs for a given chip functionality. Another factor in the
advancement of semiconductor manufacturing technology is the size of the wafers. Equipment
manufacturers are continuously trying to increase the wafer size, which increases the number of
chips to be made at once and produces higher yields, which in turn reduces the unit
manufacturing cost. As semiconductor technologies advance, the company must be prepared to
switch manufacturing capability to the newer technologies. These transitions take time and they
must be anticipated correctly. A premature transition will lead to costly under utilization, or
forcing manufacturing to use newer, more expensive equipment to manufacture older
technologies that do not generate expected revenue. An overdue transition lead to missed market
opportunities, which also lead to lower ROI for the capital investment.

A third factor unique to the semiconductor industry is that manufacturing capacity often
suffers high variability. The aggregate notion of manufacturing capacity used during strategic
planning is in reality an approximation at best. Given a particular capacity configuration for
each clean room, the manufacturing manager still has much flexibility in how that capacity is
utilized, and his/her decision will determine what the effective capacity ultimately is. For
instance, newer equipment can typically be used to manufacture older technologies, albeit at a
lower cost efficiency. Further, the effective capacity to manufacture the same technology is
different in each location, depending upon the technology mixture (capacity configuration), the
wafer size made in that facility, skill level of the labor, and myriad other factors. This is further
complicated by con