The global semiconductor manufacturing industry is facing amplified levels of competition and consolidation. As a result, there's an increasing urgency to drive productivity enhancements that support long-term success. To manage rising cost pressures and augment profitability, semiconductor enterprises need to underline and pursue strategies for end-to-end semiconductor yield improvement. Yield optimization is pivotal to curbing manufacturing costs and securing a competitive advantage. However, many companies find it challenging to attain sustainable yield enhancements due to entrenched mindsets, limited data visibility, compartmentalized efforts, and a paucity of advanced analytics capabilities.As the trend towards miniaturization continues, semiconductor devices are becoming increasingly sophisticated, resulting in an escalated influence of process variability and contaminations on yield. As devices shrink, the impact of any single defect or contamination can become significantly more severe, leading to higher yield losses. Variability in parameters such as temperature, pressure, and timing can lead to inconsistency in device performance, affecting both yield and product quality. Additionally, contaminations can originate from various sources such as materials, process gases, or the manufacturing environment itself, causing defects in the devices that result in yield loss.

1. Strategizing Sustainable Yield
Improvements in the Global
Semiconductor Industry
https://yieldwerx.com/

2. The global semiconductor manufacturing industry is facing amplified levels of competition and consolidation. As a result, there's an increasing
urgency to drive productivity enhancements that support long-term success. To manage rising cost pressures and augment profitability,
semiconductor enterprises need to underline and pursue strategies for end-to-end semiconductor yield improvement. Yield optimization is
pivotal to curbing manufacturing costs and securing a competitive advantage. However, many companies find it challenging to attain sustainable
yield enhancements due to entrenched mindsets, limited data visibility, compartmentalized efforts, and a paucity of advanced analytics
capabilities.
As the trend towards miniaturization continues, semiconductor devices are becoming increasingly sophisticated, resulting in an escalated
influence of process variability and contaminations on yield. As devices shrink, the impact of any single defect or contamination can become
significantly more severe, leading to higher yield losses. Variability in parameters such as temperature, pressure, and timing can lead to
inconsistency in device performance, affecting both yield and product quality. Additionally, contaminations can originate from various sources
such as materials, process gases, or the manufacturing environment itself, causing defects in the devices that result in yield loss.
Data-Driven Improvement Initiatives and Advanced Analytics
The era of big data and advanced analytics has provided a new pathway for yield improvement. Advanced analytics can help in predicting yield
outcomes, identifying the root causes of yield loss, and formulating strategies for improvement. Semiconductor companies are increasingly
leveraging machine learning and data mining techniques to identify patterns and correlations in large datasets, leading to more effective yield
enhancement systems.
Data-driven initiatives involve the use of data at every stage of the semiconductor manufacturing process. These include real-time data collection
during wafer fabrication, statistical analysis of process and test data, as well as predictive modeling of yield and performance metrics. Such
initiatives allow for a more holistic view of the manufacturing process, identifying the areas with the greatest loss impact, and directing
improvement efforts accordingly.

3. Overcoming Limitations of Traditional Yield Improvement Approaches
In a world where devices are shrinking in size while becoming technologically sophisticated, the effects of process variability and contaminations
on yield are intensifying. Traditional approaches for yield improvement, including focusing on yield percentages, specific product families, or
excursion cases, have limitations. While these strategies can capture and control different types of yield losses, they may not exhaust all
possibilities for enhancing profitability.
Collaboration between Engineering and Finance: A Holistic View of Yield Loss Cost
A comprehensive view of yield loss cost requires a collaborative effort between the engineering and finance departments. The engineering
department typically focuses on technical aspects such as process optimization, equipment efficiency, and defect reduction. In contrast, the
finance department focuses on the financial impacts of yield loss, including the cost of scrapped wafers, rework, and delayed deliveries.
By merging cost data from both departments, semiconductor companies can gain a more comprehensive understanding of yield loss costs. This
could include the cost of raw materials, energy, labor, and capital equipment, as well as the opportunity costs associated with yield loss. Such a
holistic view can facilitate more effective decision-making, resource allocation, and strategic planning for yield improvement.
Systemic Improvements: Focus on Machine, Man, Material, Measurement, and Method
To achieve sustainable yield improvements, it's crucial to focus on systemic issues rather than isolated problems. This involves a comprehensive
focus on the key improvement themes: machine, man, material, measurement, and method.
Machine: Machine variability can be a major contributor to yield loss. This can be mitigated through equipment optimization, preventive
maintenance, and regular performance and semiconductor yield monitoring.
Man: The role of the workforce in yield improvement cannot be underestimated. This includes operator training, skill development, and fostering
a culture of continuous improvement.
Material: The quality of raw materials and process gases can significantly affect yield. Material quality management and supplier collaboration are
key strategies in this regard.
Measurement: Accurate and timely measurement of process parameters is crucial for maintaining process control and minimizing variability.
Method: Lastly, the methods or processes used in device fabrication need to be continually optimized to improve yield.

4. Implementing Systemic Improvements for Sustainable Yield Enhancements
Next comes the phase of implementing systemic improvements to counter the recognized loss areas. Engineers concentrate on key improvement
themes revolving around five factors - machine, man, material, measurement, and method. True and false rejects are scrutinized, and the
potential for cross-functional collaborations is explored. By tackling systemic issues, semiconductor companies can actualize sustainable yield
enhancements. In some cases, external involvement could be necessary, especially for machine variability initiatives. These collaborations could
involve discussions with vendors to enhance equipment performance.
Conclusion
In the highly competitive and consolidated semiconductor industry, prioritizing end-to-end yield improvement is a strategic necessity. By adopting
a data-driven approach, companies can effectively manage cost pressures, sustain higher profitability, and gain a competitive edge in the industry.
Furthermore, a collaboration between engineering and finance departments can provide a more comprehensive view of yield loss costs, enabling
more effective decision-making and resource allocation.
Systemic improvements, focusing on machine, man, material, measurement, and method, can lead to sustainable yield improvements. Ultimately,
success in yield improvement depends on a company's ability to leverage data, advanced analytics, and cross-functional collaboration to drive
continuous improvement in its manufacturing processes.
References
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Classification," in 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Diego, CA, 2018, pp. 1-6, doi:
10.1145/3240765.3240831.
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Hodges, H. G. Jackson, and R. A. Saleh, Eds., 2003.
3. M. Taouil, S. Hamdioui, "Yield Improvement by Tolerating Defects," in 2018 31st International Conference on VLSI Design and 2018 17th
International Conference on Embedded Systems (VLSID), pp. 209-214, Jan. 2018, doi: 10.1109/VLSID.2018.00047.
4. S. Borkar, "Designing Reliable Systems from Unreliable Components: The Challenges of Transistor Variability and Degradation," in IEEE
Micro, vol. 25, no. 6, pp. 10-16, Nov.-Dec. 2005, doi: 10.1109/MM.2005.110.