The semiconductor manufacturing industry has been the cornerstone of modern technology, enabling the digital transformation of various sectors. Today, the industry stands at a crossroads where increasing complexity in chip designs, rising demands for performance and power efficiency, and shrinking feature sizes are leading to higher production costs and challenging the paradigms of semiconductor data manufacturing. Central to overcoming these challenges is the critical role of data — an essential asset that could drive process optimization, quality control, and cost efficiency in the production line. The task at hand for semiconductor industries is to harness and interpret the enormous volume of data being generated throughout various manufacturing stages to improve yield and reliability.

1. Leveraging Manufacturing Data
to Boost Semiconductor
Reliability and Yield
https://yieldwerx.com/

2. The semiconductor manufacturing industry has been the cornerstone of modern technology, enabling the digital transformation of various
sectors. Today, the industry stands at a crossroads where increasing complexity in chip designs, rising demands for performance and power
efficiency, and shrinking feature sizes are leading to higher production costs and challenging the paradigms of semiconductor data
manufacturing. Central to overcoming these challenges is the critical role of data — an essential asset that could drive process optimization,
quality control, and cost efficiency in the production line. The task at hand for semiconductor industries is to harness and interpret the enormous
volume of data being generated throughout various manufacturing stages to improve yield and reliability.
Harnessing Multifaceted Data
Semiconductor manufacturing is a multi-faceted process involving numerous stages — from initial design and fabrication to assembly, packaging,
and testing. Each of these stages generates a wealth of data that is a treasure trove of insights into the process and product performance. For
instance, semiconductor manufacturers collect in-situ, real-time data from equipment, inline metrology data, inline defect inspection data, and
test data at various stages such as wafer sort, final test, and burn-in.
Furthermore, manufacturers are increasingly integrating Process, Voltage, and Temperature (PVT) sensors within the chips, which provide real-
time data about the chip’s behavior in the field. These sensors, along with others designed to monitor reliability risks due to accelerated aging
and mechanical stress changes, provide a broader spectrum of data that could be invaluable in predicting and enhancing chip reliability.
Data Integration Challenges
However, the task of integrating this vast assortment of data poses significant challenges due to the multi-vendor environment and the
proprietary nature of certain data. The data about a die's fitness often has to move through multiple hands, from designers to foundries to
assembly and test houses. Each of these entities operates different types of equipment that generates data of uneven quality and quantity,
making data integration a daunting task.
Moreover, proprietary constraints often limit full access to all data, posing additional hurdles in achieving comprehensive data integration.
Consequently, the industry is tasked with establishing an infrastructure that can bring together and analyze this data from different sources and
processes. These efforts need to strike a balance between protecting proprietary information and allowing necessary data sharing for optimizing
process flows and improving chip quality.

3. The Need for Holistic Inspection and Testing
As chip designs continue to grow in size and complexity, devices are tested on a more piecemeal basis, in what is essentially a divide-and-conquer
approach. However, this method may fall short of providing a comprehensive view of the overall system performance.
The use of internal data from working chips has been identified as a possible custom yield management solution to this issue. By supplementing
process data with internal data from a working chip, manufacturers can gain a more holistic view of the manufacturing process. Such a unified
data-driven approach could potentially help in identifying intricate defect patterns, improving process control, and enhancing the overall yield and
reliability of the semiconductors.
The Packaging Challenge
With the benefits of Moore's Law tapering off, packaging has become a pivotal area in semiconductor manufacturing yield. Increasingly,
chipmakers are resorting to more customized and heterogeneous designs and architectures to maintain performance gains. This shift towards
complex packaging has introduced additional challenges, particularly in tracking the origin of chips, understanding the impact of assembly
processes, and ensuring full traceability. As the assembly process becomes increasingly complex, the rate of failures attributed to packaging has
escalated, especially in sectors like the automotive industry where reliability is critical.
Material Precision and Purity
Another aspect demanding attention in semiconductor manufacturing is the precision and purity of materials used. From the substrate and
Redistribution Layer (RDL) to dielectric thin films, these materials need to be pure and applied with atomic-level precision. This is due to the
escalating performance requirements that are leading to a sharp increase in manufacturing costs. The focus is thus shifting towards robust
scrap/waste reduction programs, which require an exact understanding of the material properties and thorough control of the process
parameters.

4. Inspection and Metrology: Opportunities and Implications
The drive for deeper inspection and more accurate metrology has led to the exploration of advanced techniques like atomic force microscopy,
which can provide nanoscale resolution. These more extensive and deeper inspections have the potential to generate large amounts of data,
which, when utilized efficiently, can unlock new opportunities for yield and reliability improvements in the semiconductor manufacturing
industry.
Conclusion
In the face of escalating complexity, the semiconductor manufacturing industry needs to harness the power of data more effectively. Achieving
this will require overcoming challenges associated with data integration, evolving new testing methodologies, understanding the complex world
of packaging, managing material purity and precision, and harnessing the power of advanced inspection and metrology techniques. While these
challenges are considerable, the potential rewards in terms of enhanced yield, reliability, and cost efficiency make it a worthwhile pursuit.
References:
1. V. Agrawal, Semiconductor Manufacturing Technology, 2nd Edition, Prentice Hall, 2003.
2. R. Schaller, "Technological innovation in the semiconductor industry: a case study of the International Technology Roadmap for
Semiconductors (ITRS)", Technological Forecasting & Social Change, 2004.
3. M. Taouil, et al. "Built-in Self-Test for Digital Memories," in IEEE Design & Test, vol. 37, no. 3, pp. 8-16, June 2020.
4. J. Soden et al., "Predictive maintenance and yield improvement in semiconductor manufacturing," Proc. IEEE SoutheastCon 2018, St.
Petersburg, FL, 2018, pp. 1-7.