THE COMPETITIVE SEMICONDUCTOR MANUFACTURING HUMAN
Second Interim Report
Clair Brown, Editor
12.2 Learning by Doing in Semiconductor Manufacturing
In contrast to many early studies, reductions in direct labor hours
are not the source of learning by doing in the semiconductors. Direct
labor typically makes only a small portion of variable costs and
there is little evidence that direct labor hour requirements fall
as the fab gains experience with the process. Instead, it is the
elimination of yield losses that results in substantial reductions
in manufacturing costs. It is not uncommon for yields for new semiconductor
processes to start as low as 10%. In such cases, the cost of scrapped
output is initially very high but falls to low levels as yields
rise to 90% or higher over time. Since yield improvements are typically
based on identifying and eliminating sources of yield loss, the
cost reductions are permanents characteristic of learning by doing.
Typically, learning by doing is modeled as though experience itself
is sufficient to lower costs. As a result, most studies focus on
the choice of an appropriate proxy for experience. Cumulative volume
is by far the most common choice, while other variables include
time, investment, and changes in equipment. Since yield improvements
are the principle source of learning by doing in the semiconductor
industry, it is questionable whether experience alone (as proxied
by cumulative volume) is sufficient to characterize the learning
curve. Yield improvements do not occur as the result of improved
labor productivity, but rather as the product of deliberate activities
aimed at overcoming sources of yield loss.
The microscopic dimensions associated with semiconductor processes
mean that yield problems typically cannot be resolved through repetition.
The only case where manufacturing experience is likely to improve
yields is line yield losses where wafers are dropped and broken.
Here the cause is easily identified, and we would expect that time
and experience will improve the wafer handling skills of operators.
In contrast, line yield losses where the wafers are misprocessed
(incorrect recipe or skipped steps) are not generally observable
to the laborers who cause them and have to be identified by engineering
analysis. This problem is even more prevalent with die yield losses.
Microscopic particles are sufficient to cause failed dice but are
unobservable to direct laborers indeed, the operators may unknowingly
be the source of the destructive, particles. Parametric problems
are similarly difficult to identify, when they occur.
Learning by doing, in this context, is the result of analysis by
engineers to discover the sources of yield problems and implement
solutions. In the case of parametric processing problems, the role
of engineers is most obvious as they work to identify the unknown
parameters through experimentation and analysis. Finding sources
of microscopic particles is similarly challenging and requires tests
and analysis. For line yield losses, engineers work to install equipment
and processing systems that reduce the opportunities for mistakes.
Even with broken wafers that are easily observed, engineers are
often involved in devising and implementing automated wafer handling
equipment to reduce mistakes. Thus, as Hatch and Reichelstein (1994)
have shown, cumulative production volume and cumulative engineering
analysis are the key determinants of learning by doing in semiconductor
manufacturing. These variables are not proxies for manufacturing
experience, but rather represent the means by which yields and costs
All semiconductor firms perform engineering analysis of production
volume to improve yields, but some are much more successful than
others. Figure 12.1 shows the defect density
trends for several manufacturing processes. It is clear that the
levels and rates of improvement in defect densities vary widely
between the different processes. The main hypothesis of this study
is that some of the differences in rates of learning by doing are
a result of differences in human capital and the human resource
management practices of the fabs.
While capital equipment influences yields, ultimately learning has
to be done by people. The key to learning by doing, and the competitive
advantage that accompanies it, is not just acquiring process-specific
knowledge, but also acquiring the fab-level knowledge and skills
that enhance the ability to learn. The fabs in the CSM study differ
substantially in their attitudes about and practices for acquiring
human capital. In many cases, equipment operators are expected only
to push buttons and load wafers into machines. In other fabs, operators
are given responsibilities for equipment maintenance, repair, process
control, and limited yield analysis. The fabs invest in the human
capital of these operators by training them in math, literacy, statistical
process control, and specifics about their equipment and the manufacturing
processes. The benefit is not only that operators with more human
capital are more efficient and make fewer mistakes, but they become
effective problem solvers. This is important for several reasons.
First, including operators in problem solving activities brings
more information under scrutiny. Because the operators work most
closely with the equipment, they know much about it. They can hear
or see problems with the equipment before they lead to serious yield
problems. In some fabs, equipment failures are times for operators
to take unscheduled breaks, so little is done to prevent these failures
or the yield problems that typically accompany them. In other fabs,
operators are trained and given responsibility for equipment performance,
process control, and yields. They use the information they have
from their close association with the equipment to solve problems
quickly or to bring in help before the equipment fails. In these
fabs, operators often form teams and work together to solve yield
problems. With their additional information of the idiosyncratic
traits of the equipment, they are an effective resource for preventing
and solving yield problems.
Second, investment in operator training is also useful if it frees
engineers to work on more difficult challenges. Many engineers complain
that too much of their time is spent in "firefighting,"
i.e., dealing with the myriad of minor crises that temporarily bring
production to a standstill, and some of these problems can be dealt
with by trained operators. For example, in many fabs, operators
are trained in SPC and provided with guidelines for how to identify
and solve simple out-of-control situations with their equipment.
Similarly, some operators have reported that they spend 30 40% of
their time "troubleshooting" yield problems. One fab reported
that in the case of a few wafers -with low die yields, the wafers
will be given to a group of operators to identify the source and
propose solutions. In whatever form, problem solving on the part
of operators frees engineers to do more fundamental work such as
integrating SPC databases to allow the correlation of SPC data to
The third, and perhaps most important, benefit of investing in human
capital is that fabs are able to integrate the entire manufacturing
staff into one large problem solving organization. The specific
knowledge of more individuals is included in the analysis. The entire
organization shares the task and responsibility of yield improvement.
The notion of a problem-solving organization is especially important
in the context of new manufacturing processes where a fab full of
trained employees can tackle the difficulties of learning the new
process and equipment with the result of high initial yields that
continue to improve.
Operator turnover is the, antagonist of successful investment in
human capital and efforts to enhance the rate of learning by doing.
When trained and experienced operators leave the fab, they take
their knowledge of the processes and equipment with them. Hence,
turnover represents knowledge leaving the fab. High turnover creates
an additional impediment for learning by doing, because, new employees
require extra training and are more prone to make mistakes. The
rate of learning by doing will likely fall since the production
staff makes more mistakes and requires resources for training that
could otherwise be dedicated to yield improvement.
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