A Data-Driven Approach for Real-Time Bottleneck Detection and Optimization in Semiconductor Manufacturing Using Active Period Method and Visualization

Min Yin

doi:10.70393/616a6e73.333534

OPEN ACCESS |Research Article ||3 December 2025

A Data-Driven Approach for Real-Time Bottleneck Detection and Optimization in Semiconductor Manufacturing Using Active Period Method and Visualization

Min Yin

University of California-Berkeley

gmiayinc@gmail.com

University of California-Berkeley, 94720, USA.

* Corresponding Author¹: Min Yin, E-Mail: gmiayinc@gmail.com

Publication

Accepted 2025 December 3 ; Published 2025 December 3

Academic Journal of Natural Science, 2025, 2(4), 3078-5170.

Abstract

With the rapid development of the semiconductor industry, identifying and optimizing bottlenecks is crucial for improving production line efficiency. This paper proposes a method combining Activity Cycle Method (APM) and data visualization techniques. APM identifies key bottlenecks in semiconductor manufacturing by analyzing the continuous uptime of machines and the duration of their activity cycles. Data visualization tools are then used to present these key bottlenecks in an intuitive and actionable manner. Applying both methods to a real-world semiconductor manufacturing environment significantly improves production efficiency and machine utilization, making this method practically applicable in semiconductor manufacturing.

Keywords

Semiconductor Manufacturing , Bottleneck Detection , Active Period Method , Data Visualization , Process Optimization , Machine Utilization , Heatmap , Time-Series Analysis .

Metadata

DOI:

10.70393/616a6e73.333534

ARK:

ark:/40704/AJNS.v2n4a03

Pages: 19-26

References: 27

Disciplines: Computer Science

Subjects: Data Science

Cite This Article

APA Style

Yin, M. (2025). A data-driven approach for real-time bottleneck detection and optimization in semiconductor manufacturing using active period method and visualization. Academic Journal of Natural Science, 2(4), 19-26. https://doi.org/10.70393/616a6e73.333534

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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