Samsung electronics co., ltd. (20240319259). ELECTRICAL DEVICE RELIABILITY PROPERTIES PREDICTION DEVICE AND ELECTRICAL DEVICE RELIABILITY PROPERTIES PREDICTION METHOD simplified abstract
ELECTRICAL DEVICE RELIABILITY PROPERTIES PREDICTION DEVICE AND ELECTRICAL DEVICE RELIABILITY PROPERTIES PREDICTION METHOD
Organization Name
Inventor(s)
Yeonjeong Kim of Suwon-si (KR)
Hyungkeun Yoo of Suwon-si (KR)
Kyunghoon Lee of Suwon-si (KR)
ELECTRICAL DEVICE RELIABILITY PROPERTIES PREDICTION DEVICE AND ELECTRICAL DEVICE RELIABILITY PROPERTIES PREDICTION METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240319259 titled 'ELECTRICAL DEVICE RELIABILITY PROPERTIES PREDICTION DEVICE AND ELECTRICAL DEVICE RELIABILITY PROPERTIES PREDICTION METHOD
Simplified Explanation: The patent application describes a method for predicting electrical reliability properties by generating optical spectrum data, performing a wafer level reliability process, measuring electrical reliability property data, and using this data to train a prediction model to detect predicted electrical reliability property data based on optical data.
Key Features and Innovation:
- Generation of optical spectrum data of a substrate
- Wafer level reliability process
- Measurement of electrical reliability property data
- Matching inspection region to optical spectrum data and electrical reliability property data
- Training an electrical reliability properties prediction model
- Extracting feature vectors from target optical data
- Predicting electrical reliability property data based on feature vectors
Potential Applications: This technology can be applied in the semiconductor industry for predicting electrical reliability properties of substrates, improving quality control processes, and enhancing overall product performance.
Problems Solved: This technology addresses the need for accurate prediction of electrical reliability properties in semiconductor manufacturing, which can lead to improved product quality and reliability.
Benefits:
- Enhanced quality control in semiconductor manufacturing
- Improved product performance and reliability
- Efficient prediction of electrical reliability properties
- Streamlined manufacturing processes
Commercial Applications: The technology can be utilized in semiconductor manufacturing companies to optimize production processes, reduce defects, and ensure high-quality products for various electronic applications.
Prior Art: Readers can explore prior research in the fields of semiconductor manufacturing, optical data analysis, and predictive modeling to understand the existing knowledge related to this technology.
Frequently Updated Research: Stay informed about the latest advancements in semiconductor manufacturing, reliability prediction methods, and optical data analysis to enhance the application of this technology.
Questions about Electrical Reliability Properties Prediction: 1. How does the method of generating optical spectrum data contribute to predicting electrical reliability properties? 2. What are the key steps involved in training the electrical reliability properties prediction model?
By incorporating optical spectrum data, performing a wafer level reliability process, and training a prediction model, this technology offers a comprehensive approach to predicting electrical reliability properties in semiconductor manufacturing.
Original Abstract Submitted
provided is an electrical reliability properties prediction method including generating a plurality of pieces of optical spectrum data of a substrate, performing a wafer level reliability (wlr) process on the substrate, measuring electrical reliability property data based on the wlr process, matching an inspection region to the plurality of pieces of optical spectrum data and the electrical reliability property data, generating a data set, performing data pre-processing, training an electrical reliability properties prediction model, acquiring a plurality of pieces of target optical data from a database, and extracting, with respect to the plurality of pieces of target optical data, a feature vector from the plurality of pieces of target optical data, and detecting predicted electrical reliability property data of the plurality of pieces of target optical data based on the feature vector.
