Yield Intelligence for High-Density MicroLED Manufacturing

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Challenge

A leading microLED manufacturer faced an unprecedented data challenge driven by next-generation display architectures. Each die contained over 3 million individual pixels, with every pixel generating multiple electrical, optical, and inspection measurements throughout the manufacturing lifecycle—resulting in over 10 billion data points per wafer.

Conventional yield systems—built for die- and wafer-level analysis—were no longer sufficient. The manufacturer required a fundamentally new approach: a yield platform capable of operating at the pixel level as the primary unit of analysis, delivering true spatial resolution and deep parametric insight at extreme scale.

Solution

The solution enabled seamless ingestion and unification of heterogeneous datasets across the manufacturing flow—including electrical test data from ASIC wafers, high density pixel data from MicroLED display panels, and process data from the bonding stage.

A key challenge addressed was the alignment of fundamentally different data structures and coordinate systems—ranging from die-level electrical measurements to millions of pixel-level data points per display panel die. The platform normalized and spatially mapped these datasets into a single, coherent analytical framework, preserving full traceability across all domains.

In addition, the solution ingested KLARF-based defect and metrology data, including associated inspection images, enabling direct overlay of defect signatures onto electrical and optical test results. This unified view empowered engineers to perform advanced analytics such as root cause identification, killer defect ratio analysis, and spatial correlation across process, defect, and test domains.

Results: Unified Analytics for AR/VR MicroLEDs

Integrated Data Foundation

Seamless fusion of wafer images and pixel-level test data.

AI-Driven Intelligence

Automated outlier detection and scalable defect classification

Pixel-Optimized Control

Advanced limits management and binning at pixel resolution

End-to-End Traceability

Full visibility from wafer → pixel → module → device

Cloud-Scale Performance

Petabyte-scale processing for high-density pixel datasets

Quality & Reliability Impact

Enhanced quality, reliability, and supply chain traceability

TCO & ROI Optimization

Reduced infrastructure costs, faster yield learning, and accelerated time-to-market
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Features Section
Device Layout Integration
Define ASIC and Display Panel Wafer Bonding Rules & Criteria - Normalizing Coordinates - including Coupon Die/Test Structures
Pixel-Level Clustering Analytics
Detailed Pixel Level Analysis - for different testing patterns, frequencies, voltages on 10Billion+ data points. Automatically identifying random and systematic failing pixels, dim, bright and heat maps and more.
ASIC & Pixel Data Analysis

The solution enabled bonded wafer analysis by coupling electrical data, pixel-level data, and test structures into a unified framework. This allowed precise correlation of failing bonded die, bridging ASIC performance with pixel behavior.

Engineers can drill down to individual data elements to identify root causes of yield loss, including parametric drift, spatial anomalies, and dead or dim pixels—linking failures directly to process variations, bonding issues, or physical defect.

AI enabled data analysis that digs deeper into the data and finds anomalies that were previously not detected.

Statistical & Visualization Tools

Parametric wafer maps, heat maps, defect maps, histograms, box plots, and trend charts—along with statistical calculations such as quartile and interquartile range (IQR)—were enhanced for greater accuracy and reliability.

Improvements to chart rendering, data grouping, limit handling, and die-level filtering ensured consistent analysis and robust, production-ready workflows

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