This article is the second in a series from PDF Solutions on why adopting big data platforms will transform the compound semiconductor industry. The first part “Accelerating silicon carbide (SiC) manufacturing with big data platforms” was recently published on EDN.
Compound semiconductors such as SiC are revolutionizing industries with their ability to handle high-power, high-frequency, and high-temperature technologies. However, as they climb in demand across sectors like 5G, electric vehicles, and renewable energy, the manufacturing challenges are stacking up. The semiconductor sector, particularly with SiC, trails behind the mature silicon industry when it comes to adopting advanced analytics and streamlined yield management systems (YMS).
The roadblock is high defectivity levels in raw materials and complex manufacturing processes that stretch across multiple sites. Unlocking the full potential of compound semiconductors requires a unified and robust end-to-end yield management approach to optimize SiC manufacturing.
A variety of advanced tools, industry approaches, and enterprise-wide analytics hold the potential to transform the growing field of compound semiconductor manufacturing.
Addressing challenges in compound semiconductor manufacturing
While traditional silicon IC manufacturing has largely optimized its processes, the unique challenges posed by SiC and other compound semiconductors require targeted solutions.
- Material defectivity at the source
Unlike silicon ICs, where costs are distributed across numerous fabrication steps, SiC manufacturing sees the most significant costs and yield challenges in the early stages of production, such as crystal growth and epitaxy. These stages are prone to producing defects that may only manifest later in the process during electrical testing and assembly, leading to inefficiencies and high costs.
As material defects evolve during manufacturing, traceability is essential to pinpoint their origin and mitigate their impact. Yet, the lack of robust systems for tracking substrates throughout the process remains a significant limitation.
- Siloed data and disparate systems
Compound semiconductor manufacturing often involves multi-site operations where substrates move between fabs and assembly facilities. These operations frequently operate on legacy systems that lack standardization and advanced data integration capabilities.
Data silos created by disconnected manufacturing execution systems (MES) and statistical process control (SPC) tools hinder enterprises from forming a centralized view of their production. Without cross-operational alignment enabled by unified analytics platforms, root cause analysis and yield optimization are nearly impossible.
- Nuisance defects and variability
Wafer inspection in compound semiconductors reveals a high density of “nuisance defects”—spatially dispersed points that do not affect performance but can overwhelm defect maps. Distinguishing between critical and benign defects is critical to minimizing false positives while optimizing resource allocation.
Furthermore, varying IDs for substrates through processes like polishing, epitaxy, and sawing hamper effective wafer-level traceability (WLT). Using unified semantic data models can alleviate confusion stemming from frequent lot splits, wafer reworks, and substrate transformations.
How big data analytics and AI catalyze yield management
Compound semiconductor manufacturers can unlock yield lifelines by deploying comprehensive big data platforms across their enterprises. These platforms go beyond traditional point analytics tools, providing a unified foundation to collect, standardize, and analyze data across the entire manufacturing spectrum.
- Unified data layers
The heart of end-to-end yield management lies in breaking down data silos through an enterprise-wide data layer. By standardizing data inputs from multiple MES systems, YMSs, and SPC tools, manufacturers can achieve a holistic view of product flow, defect origins, and yield drop-off points.
For example, platforms using standard models like SEMI E142 facilitate single device tracking (SDT), enabling precise identification and alignment of defect data from crystal growth to final assembly and testing.
- Root cause analysis tools
Big data platforms offer methodologies like kill ratio (KR) analysis to isolate critical defect contributors, optimize inspection protocols, and rank manufacturing steps by their yield impact. For example, a comparative KR analysis on IC front-end fabs can expose the interplay between substrate supplier quality, epitaxy reactor performance, and defect propagation rates. These insights lead to actionable corrections earlier in production.
By ensuring that defect summaries feed directly into analytics dashboards, enterprises can visualize spatial defect patterns, categorize issues by defect type, and thus rapidly deploy solutions.
- Predictive analytics and simulation
AI-driven predictive tools are vital for anticipating potential yield crashes or equipment wear that can bottleneck production. Using historical defect patterns and combining them with contextual process metadata, yield management systems can simulate “what-if” outcomes for different manufacturing strategies.
For instance, early detection of a batch with high-risk characteristics during epitaxy can prevent costly downstream failures during assembly and final testing. AI-enhanced traceability also enables companies to correlate downstream failure patterns back to specific substrate lots or epitaxy tools.
- SiC manufacturing case study
Consider a global compound semiconductor firm transitioning to 200-mm SiC wafers to expand production capacity. By deploying a big data-centric YMS across multi-site operations, the manufacturer would achieve the following milestones within 18 months:
- Reduction of nuisance defects by 30% post-implementation of advanced defect stacking filters.
- Yield improvement of 20% via optimized inline inspection parameters identified from predictive KR analysis.
- Defect traceability enhancements enabling root cause identification for more than 95% of module-level failures.
These successes underscore the importance of incorporating AI and data-driven approaches to remain competitive in the fast-evolving compound semiconductor space.
Building a smarter compound semiconductor fabrication process
The next frontier for compound semiconductor manufacturing lies in adopting fully integrated smart manufacturing workflows that include scalability in the data architecture, proactive process control, and an iterative improvement culture.
- Scalability in data architecture
Introducing universal semantic models enables tracking device IDs across every transformation from input crystals to final modules. This end-to-end visibility ensures enterprises can scale into higher production volumes seamlessly while maintaining enterprise-wide alignment.
- Proactive process control
Setting an enterprise-wide baseline for defect classification, detection thresholds, and binmap merging algorithms ensures uniformity in manufacturing outcomes while minimizing variability stemming from site-specific inconsistencies.
- Iterative improvement culture
Yield management thrives when driven by continuous learning cycles. The integration of defect analysis insights and predictive modeling into day-to-day decision-making accelerates the feedback loop for manufacturing teams at every touchpoint.
Pioneering the future of yield management
The compound semiconductor industry is at an inflection point. SiC and its analogues will form the backbone of the next generation of technologies, from EV powertrains to renewable energy innovations and next-generation communication.
Investing in end-to-end data analytics with enterprise-scale capabilities bridges the gap between fledgling experimentation and truly scalable operations. Unified yield management platforms are essential to realizing the economic and technical potential of this critical sector.
By focusing on robust data infrastructures, predictive analytics, and AI integrations, compound semiconductor enterprises can maintain a competitive edge, cut manufacturing costs, and ensure the high standards demanded by modern applications.
Steve Zamek, director of product management at PDF Solutions, is responsible for manufacturing gata analytics solutions for fabs and IDMs. Prior to this, he was with KLA (former KLA-Tencor), where he led advanced technologies in imaging systems, image sensors, and advanced packaging.
Jonathan Holt, senior director of product management at PDF Solutions, has more than 35 years of experience in the semiconductor industry and has led manufacturing projects in large global fabs.
Dave Huntley, a seasoned executive providing automation to the semiconductor manufacturing industry, is responsible for business development for Exensio Assembly Operations at PDF Solutions. This solution enables complete traceability, including individual devices and substrates through the entire assembly and packaging process.
Related Content
- The Road to 200-mm SiC Production
- Silicon carbide (SiC) counterviews at APEC 2024
- Wolfspeed to Build 200-mm SiC Wafer Fab in Germany
- Silicon carbide’s wafer cost conundrum and the way forward
- Reducing Costs, Improving Efficiency in SiC Wafer Production with CMP
The post Unlocking compound semiconductor manufacturing’s potential requires yield management appeared first on EDN.
