Advanced Packaging
Yield Traceability

Tango AI — Cross-Stage Yield Traceability Solution for Advanced Packaging
Breaking down data silos and enabling full visibility into yield evolution from wafer to packaging.

In an era dominated by advanced packaging technologies such as 2.5D, 3D IC, Chiplet, and Fan-Out, yield issues are no longer confined to a single process step—they are the result of tightly interconnected stages across wafer fabrication, packaging, and testing.

Tango AI, powered by a robust big data engine, integrates data from WIP, WAT, CP, BUMP, ASSY, FT, and SLT to construct a comprehensive product yield traceability map—enabling enterprises to accurately pinpoint defect origins and accelerate process optimization.

Key Feature Highlights:

✦ Cross-Stage Data Correlation

Automatically associate wafer lots, die numbers, packaging lots, and test batches to achieve end-to-end traceability from front-end wafer fabrication to back-end packaging and testing.

✦ Real-Time Anomaly Traceback Analysis

When failures are detected at FT or SLT, Tango AI can quickly trace back to the BUMP, CP, and even WAT levels—helping to rapidly identify the affected areas and failure characteristics.

✦ Yield Trend Evolution Visualization

Present yield variations from wafer to finished product using diverse visualizations such as trend charts, heat maps, and scatter plots—enabling early detection of subtle anomaly trends.

✦ Intelligent Anomaly Correlation Modeling

Utilize statistical computation and machine learning algorithms to uncover potential correlations between process parameters and test anomalies—enhancing the efficiency and accuracy of defect analysis.

Benefits

Shorten anomaly analysis and improvement time
Reduce systemic failure risks and improve shipment yield
Strengthen quality control and data transparency across multiple process stages
Support large-scale data analytics for next-generation packaging technologies (e.g., HBM, CoWoS, FOPLP)

Tango AI — The Yield Traceability Hub Built for the Era of Advanced Packaging

From wafer to packaging, from testing to shipment, Tango AI allows users to define data interpretation rules and establish UID information—including wafer lot ID, wafer ID, and wafer XY coordinates. With our platform, every individual chip can be searched in real time.

Tango AI empowers you to build cross-stage, cross-site, and cross-product data assets, ensuring full traceability of each die’s history and data-driven insights for every process improvement.

Success Story: Implementing Tango AI Technology
Reduced anomaly analysis time by 70% and improved shipment yield by over 2%.

Case

A leading global IDM semiconductor company specializing in advanced automotive electronics and communication chips adopted 2.5D advanced packaging technology (Heterogeneous Integration).
At the early stage of mass production, the company faced the following challenges:

★ Lengthy Anomaly Analysis Time

Tracing FT (Final Test) failures back to the BUMP/WAT stages typically required 7 to 10 working days.

★ Yield loss points were difficult to pinpoint accurately.

Data scattered across process stages made it difficult to identify anomaly correlations in real time.

★ Tight customer shipment schedules imposed significant time pressure.

The OEM required faster anomaly response times and improved shipment stability.

Implementing the Tango AI Cross-Stage Data Traceability Solution

The company chose to deploy Tango AI to integrate WIP, WAT, CP, BUMP, ASSY, FT, and SLT process data through a centralized big data platform, supported by the following capabilities:

★ Automated Data Correlation Modeling

Automatically correlate test and process data across stations based on Die ID, Package ID, and Lot ID.

★ Real-Time Anomaly Traceback Analysis

When a fail bin is detected at FT, critical parameter anomalies can be instantly traced back to the CP or BUMP level.

★ Visualization of Yield Evolution Trends

Leverage visual tools such as trend charts, wafer maps, and box plots to quickly review yield variation patterns across batches.

★ Intelligent Anomaly Correlation Analysis

Leverage machine learning models (e.g., PCA, clustering) to uncover hidden correlations between process anomalies and test failures.

Results

The company chose to deploy Tango AI, leveraging a big data platform to integrate process data across all key stages—WIP, WAT, CP, BUMP, ASSY, FT, and SLT

supported by the following features:

★ Analysis time reduced by over 70%

Average anomaly traceback time was reduced from 7–10 days to just 2–3 days, with critical cases resolved within a few hours.

★ Shipment yield improved by over 2%

Through rapid anomaly mitigation and process fine-tuning, average FT yield improved by over 2%
Batch stability significantly increased, leading to a noticeable reduction in customer complaints and RMA cases.

★ Increased customer trust and deeper collaboration.

Audit pass rates from OEM and Tier 1 customers improved, accelerating the New Product Introduction (NPI) process.
The company became the preferred supplier for next-generation packaging technologies in the automotive and communications sectors.

Conclusion

This case demonstrates that by combining cross-stage yield data integration with intelligent anomaly traceability, companies can not only drastically reduce anomaly resolution time but also significantly improve shipment quality and customer satisfaction. It establishes a robust defense against reliability risks during the adoption of advanced packaging technologies.

Advanced Packaging Yield Traceability

Tango AI — Cross-Stage Yield Traceability Solution for Advanced Packaging Breaking down data silos and enabling full visibility into yield evolution from wafer to packaging.