What is Tango AI?
Tango AI is a provider specializing in web-native software and services designed to enhance semiconductor yield. Its system focuses on yield improvement by offering a range of functions, including parameter analysis, yield rate visualization, and test item monitoring through various charts. This approach aims to reduce manual effort, increase accuracy, and facilitate faster collaborative problem-solving within semiconductor manufacturing processes.
The platform integrates different modules like Big Data handling (supporting various sources and formats), TTR (Test Time Reduction) for test item correlation and recipe control, PAT (Part Average Testing) for automotive device requirements and outlier detection, and specific Tango AI features like map classification and yield prediction. These tools collectively contribute to optimizing production efficiency and quality control in the semiconductor industry.
The platform integrates different modules like Big Data handling (supporting various sources and formats), TTR (Test Time Reduction) for test item correlation and recipe control, PAT (Part Average Testing) for automotive device requirements and outlier detection, and specific Tango AI features like map classification and yield prediction. These tools collectively contribute to optimizing production efficiency and quality control in the semiconductor industry.
Features
- Big Data Analysis: Handles diverse data sources (inline/pcm/cp/ft/defect) and formats (text/xls/stdf/csv), enabling genealogy correlation and advanced analysis.
- Test Time Reduction (TTR): Correlates test items, allows real-time recipe file control, and includes VM monitoring.
- Part Average Testing (PAT): Meets automotive device requirements (AEC Q-001 Rev-D) and implements outlier detection methods.
- AI Capabilities: Performs map classification and yield prediction.
- Yield Enhancement System: Provides various charts for visualizing yield rates and monitoring testing items.
Use Cases
- Improving semiconductor manufacturing yield rates.
- Analyzing semiconductor test parameters.
- Visualizing production yield data.
- Monitoring semiconductor testing items.
- Detecting outliers in semiconductor testing (PAT).
- Predicting semiconductor yield using AI.
- Classifying wafer maps using AI.
- Meeting automotive semiconductor quality standards (AEC Q-001).
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