Semiconductor Package Inspection with Predictive Model for Wirebond High Frequency Performance
JP2025514512A5Pending Publication Date: 2026-05-25VIASAT INC
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Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- VIASAT INC
- Filing Date
- 2023-05-04
- Publication Date
- 2026-05-25
AI Technical Summary
Technical Problem
The existing semiconductor package manufacturing process faces challenges in detecting negative performance effects of wire bond interconnects, leading to costly and time-consuming reworking and retesting.
Method used
A machine learning-based RF performance prediction system is implemented to evaluate packages with wire bond interconnects by capturing input data, processing it using a trained machine learning model, and predicting the RF performance rating.
Benefits of technology
This approach enables early prediction of RF performance during wire bond assembly, reducing the need for costly rework and retesting, and improving manufacturing efficiency by identifying potential defects before final testing.
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Abstract
Methods, systems, and apparatuses are provided for use in semiconductor package manufacturing of a package having a die and a die substrate. The method for use in semiconductor package manufacturing includes forming one or more wirebond interconnects between the die and the die substrate, capturing input data representative of characteristics of the wirebond interconnects during inspection of the formed wirebond interconnects, and passing the captured input data to a machine learning (ML) engine. The method further includes processing the captured input data by the machine learning engine using a trained model to obtain an output sequence of data, evaluating the output sequence of data to determine a predicted radio frequency (RF) performance rating, and outputting the predicted RF performance rating. Processing the training dataset may include applying the image data and parameters to a multi-layer neural network to obtain a set of candidate ML models and selecting an optimal ML model.
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