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Cited by 380

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Process Innovation and Learning by Doing in Semiconductor Manufacturing

Hatch, Nile W., Mowery, David C.·1998·Management Science

Abstract

This paper analyzes the relationship between process innovation and learning by doing in the semiconductor industry where improvements in manufacturing yield are a catalyst for dynamic cost reductions. In contrast to most previous studies of learning by doing, the learning curve is shown here to be the product of deliberate activities intended to improve yields and reduce costs, rather than the incidental byproduct of production volume. Since some of the knowledge acquired through learning by doing during new process development is specific to the production environment where the process is developed, some knowledge is effectively lost when a new process is transferred to manufacturing. We find that dedicated process development facilities, geographic proximity between development and manufacturing facilities, and the duplication of equipment between development and manufacturing facilities are all significant in improving performance in introducing new technologies. Once in manufacturing, new processes are shown to disrupt the ongoing learning activities of existing processes by drawing away scarce engineering resources to “debug” the new processes.

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Metadata

Authors
Hatch, N. W., & Mowery, D. C.
Venue
Management Science
Published
1998-11
Volume
44
Issue
11-part-1
Pages
1461-1477
ISSN
  • 0025-1909 (ISSN-L)
  • 1526-5501
OpenAlex
W2112256364
Cited by
380
References
0
FWCI
17.83
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Process Innovation and Learning by Doing in Semiconductor Manufacturing. https://app.cochat.ai/papers/10.1287/mnsc.44.11.1461
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Hatch, N. W., & Mowery, D. C. (1998). Process Innovation and Learning by Doing in Semiconductor Manufacturing. Management Science. https://doi.org/10.1287/mnsc.44.11.1461

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.