Posted in Semiconductor Digest. Click here to view original article
By: Steve Zamek
The semiconductor industry is experiencing a paradigm shift.
While silicon has dominated the landscape for decades, compound semiconductors-materials made from two or more elements in the periodic table are rapidly emerging as the foundation for next-generation technologies. From electric vehicles to 5G infrastructure, these advanced materials are enabling innovations that were previously impossible with traditional silicon.
However, manufacturing compound semiconductors presents unique challenges that require sophisticated solutions. In this deep dive, we’ll explore how advanced data analytics and end-to-end yield management are transforming compound semiconductor manufacturing, making it more efficient, cost-effective, and reliable than ever before.
The compound semiconductor revolution
The compound semiconductor market is experiencing explosive growth, particularly in high-volume technologies like silicon carbide (SiC), gallium arsenide (GaAs), indium phosphite (InP), and gallium nitride (GaN). While these materials represent a smaller market size compared to silicon CMOS, their year over-year growth rates significantly outpace traditional semiconductor technologies.
This growth is driven by compelling advantages: compound semiconductors offer superior performance in high power, high-frequency, and high-temperature applications. They’re essential for electric vehicle power electronics, wireless communication systems, solid-state lighting, and photonic interconnects.
However, their manufacturing processes are decades behind silicon CMOS in terms of maturity and optimization.
The Big Data challenge in compound semiconductor manufacturing
Modern semiconductor manufacturing generates massive amounts of data, often terabytes per wafer across all operations. This challenge is further exacerbated in automotive applications where stringent requirements for data retention require an intricate balance between the storage costs with performance of the analytics running on large data volumes.
The manufacturing cycle involves numerous data types stored across various systems, starting with wafer manufacturing data. That includes manufacturing execution system (MES) data; equipment data and shop floor events; equipment logs and on-tool sensor traces; lot genealogy tracking wafer and lot relationships including rework and inline data combining metrology and inspection. It also considers test data such as process control monitor (PCM) e-test or wafer acceptance test (WAT). Die-level testing generates wafer sort data including bin state for each die and rich multi-dimensional parametric data. Package- and module-level data is growing in its complexity as well, as we are seeing the trend of co-packaging multiple fab technologies.
Managing this data complexity requires sophisticated big data analytics platforms that can archive, aggregate, and prepare data for meaningful analysis across the entire manufacturing flow.
What makes compound semiconductors different
The economics of compound semiconductor manufacturing fundamentally differ from silicon CMOS. In compound semiconductor production, particularly for silicon carbide, a significant portion of the total cost is consumed early in the process through expensive bare substrates or epitaxial wafers. This cost structure makes early-stage analytics and defect management critical for profitability. And being able to link the data across all manufacturing steps all the way out to final test proves critical for insights on how to improve yield.
Unlike silicon CMOS manufacturing, where relatively inexpensive substrates allow manufacturers to focus analytics efforts later in the process, compound semiconductor manufacturers must implement sophisticated monitoring and analysis from the start. This requirement has driven the development of specialized use cases and analytics approaches tailored to compound semi conductor manufacturing.
Below we consider use cases that highlight the need for compound semi conductor analytics. The first outlines the advantage of defect stacking and rebinning for root cause analysis. Traditional defect analysis on individual wafers often reveals little actionable information.
When defect data is stacked and filtered by specific attributes at the lot or product level, distinct patterns emerge that can be attributed to crystal defects or process issues in epitaxial growth. This enables manufacturers to achieve faster root cause identification, apply predictive models to anticipate downstream issues, and implement corrective actions before defects impact yield
The second use case focuses on substrate supplier quality. Here sophisticated analysis technique takes pre- and post-epitaxial defect data, aggregates it by substrate and epi supplier, and creates three-dimensional maps at the crystal level. By running multivariate screening across different attributes, manufacturers can drive substrate supplier quality improvements, identify defects originating from crystal growth, create ink maps that forward quality information through assembly, and screen out defective dies to prevent field failures.
Correlating edge process parameters to metrology results is another use case. Many fabrication facilities collect extensive equipment trace data from dozens to hundreds of on-tool sensors but rarely correlate this data with downstream metrology results.
Advanced analytics platforms can align this equipment data with MES data to track the associations between wafers, recipe runs, and specific chambers; identify root causes for process variations; enable chamber qualification after maintenance; and perform detailed root cause analysis on metrology excursions.
The fourth use case is the use of analytics to improve inline inspections. Traditionally this has been done by die-level summaries to understand how inline defects impact final yield. Compound semiconductor manufacturing presents unique complications, such as alignment of defect maps between unpatterned and patterned defect scans, performed on equipment from various vendors.
Consequently, defect scans need to be aligned to multiple binmaps, through the multiple wafer test insertions and virtual operations in burn-in, assembly and final test. Advanced data platforms now support both traditional kill ratio analysis and sophisticated machine learning models that use inline and substrate data to train against electrical parameters, enabling more accurate yield predictions and process improvements.
Use case number five describes the importance of die screening and ink maps for automotive applications. Auto motive applications demand exceptional quality levels, requiring sophisticated screening methods that combine data from multiple manufacturing steps including epitaxial defects from ma terial suppliers, wafer front-end defect maps, electrical wafer sort bin maps and parametric maps from burn-in.
By analyzing dies in this multi-dimensional space, manufacturers can implement outlier screening to prevent potentially defective parts from reaching automotive customers.
Finally, we are seeing a growing need for predictive burn-in in assembly and test as the means to increase the operational efficiency without compromising quality. The use of PCM and wafer probe results appear to provide good predictions for the outcomes of burn-in testing, optimizing test flows and reducing manufacturing costs.
Technology behind advanced yield management
Implementing these sophisticated analytics requires a comprehensive big data platform with multiple layers:
- Connectivity Layer: Standard APls and connectors that interface with various facility systems and databases, ensuring seamless data collection across the manufacturing flow.
- Data Layer and Controls: Indus try-proven data models specifically
designed for semiconductor manufacturing that align diverse data types and enable comprehensive analysis.
- Applications Layer: Sophisticated analytics applications incorporating the latest Al/ML frameworks, enabling advanced predictive modeling and real-time decision making.
- Visualization layer: A comprehensive set of mapping and charting tools making data discovery both easy and After all, seeing is believing.
- Full Traceability: Complete trace ability of any physical or logical entity in any direction-backward or forward-across all manufacturing This capability is essential given the complex nature of semiconductor manufacturing, where modules often contain multi ple fab products with various rework steps, lot recasting, and regrouping operations.
Accelerating industry maturity through advanced analytics
While the compound semiconductor industry is decades behind silicon CMOS, this gap presents a unique opportunity. By leveraging advanced data analytics proven in silicon manufacturing, compound semiconductor manufacturers can dramatically accelerate their progress.
The combination of high market growth rates and relatively immature manufacturing processes creates an ideal environment for implementing sophisticated analytics solutions.
Companies that embraced these technologies just a few years ago are now seeing significant competitive advantages as the compound semiconductor market continues its rapid expansion.
Current industry deployment Today, major compound semiconductor manufacturers – including IDMs, fabless companies, and foundries – are already implementing these advanced analytics solutions. 12 of our customers are large IDM’s processing silicon carbide, gallium arsenide, gallium nitride, and other compound semi conductor technologies, the industry is demonstrating strong adoption of sophisticated yield management approaches.
These deployments are driving continuous expansion of use cases and improvements in system usability, creating a positive feedback loop that benefits the entire compound semiconductor ecosystem.
The path forward
The compound semiconductor industry stands at a critical juncture. Market demand is driving unprecedented growth, while manufacturing challenges require increasingly sophisticated solutions. End-to-end yield management, powered by advanced data analytics, offers a clear path to manufacturing excellence.
By implementing comprehensive analytics platforms that can handle the unique challenges of compound semiconductor manufacturing-from expensive early-stage substrates to complex automotive quality requirements-manufacturers can achieve several key objectives:
- Dramatically improve yield rates across all manufacturing steps
- Improve quality and reliabili ty to meet stringent application requirements
- Improve efficiency by early discovery and root cause analysis
- Build predictive capabilities that pre vent quality issues before they occur The future of compound semiconductor manufacturing belongs to companies that can effectively harness the power of their manufacturing data. As the industry continues to mature and grow, those who invest in advanced yield management capabilities today will be best positioned to capitalize on tomorrow’s opportunities.
The revolution in compound semiconductor manufacturing is not just about new materials or processes – it’s about transforming how we understand, control, and optimize every aspect of production through the intelligent application of data analytics.
The companies that embrace this transformation will define the future of the semiconductor industry.