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- Achieved early detection of cause of defects in raw materials through AI, becoming "first to overcome this challenge in the industry"
- Applied to high-value semiconductor substrates, analyzing raw material defects in only one minute
- Reduces defect analysis time by up to 90%
Previously, incoming raw materials underwent only a visual inspection before entering the production process. However, the continued advancement of semiconductor substrate technology changed this. Even after improving all in-process defect causes, failures in reliability evaluations continued to rise. This led the quality of incoming materials to gain attention as a decisive factor affecting reliability evaluations.
The core raw materials (i.e. Prepreg (PPG), Ajinomoto Build-up Film (ABF), and Copper-Clad Laminate (CCL)) that comprise semiconductor substrates arrive as a mixture of glass fibers, inorganic compounds, and other components. In the past, air voids (gaps between particles) or foreign particles generated during the material mixing process did not significantly impact product performance. However, as substrate specifications, such as circuit spacing, have become increasingly stringent, the presence of air voids and foreign particles, depending on their size, has started to cause defects.
As a result, it is virtually impossible to identify which part of the raw material is responsible for the defect using traditional visual inspection methods, which has become a significant challenge for the industry.
If we were to compare one lot of raw materials mixture (unit of raw materials with the same characteristics that goes into the production process) to a batch of cookie dough, it is impossible for the eye to perceive the concentration of salt or sugar in a certain portion, the number of air holes in the dough, or the number of foreign particles.
LG Innotek has found a way to overcome this industry challenge with AI. Its "AI-based Inspection System for Incoming Raw Materials" has been trained with tens of thousands of pieces of data on the composition of materials that are either suitable or unsuitable for a product. Based on this, it analyzes the components and defective areas of semiconductor substrate raw materials in only one minute, with an accuracy rate of over 90%, and visualizes quality deviations in each lot of raw materials.
By using AI machine learning to visualize, quantify, and standardize material configurations optimized for quality, LG Innotek has been able to prevent defective raw materials from entering the production process. The company can change the material design based on the quality deviation information visualized by the AI system, allowing it to ensure that the quality of the raw materials lot is uniform at a suitable level before entering the process.
An LG Innotek official commented, "With the "AI-based Inspection System for Incoming Raw Materials", the time required to analyze defects has been decreased by up to 90%, and the cost of resolving the causes of defects has been significantly reduced."
LG Innotek plans to enhance the AI system's detection capabilities by sharing raw materials-related data with customers and suppliers in the substrate sector through digital partnerships.
Additionally, the company aims to expand the system's application to optical solutions, such as camera modules, where the image-based detection of material defects can play a crucial role.
LG Innotek CTO S.David Roh said, "With the "AI-based inspection system", we will complete LG Innotek's unique AI ecosystem, which delivers exceptional customer value by identifying causes of product defects early in the production process." He added, "We will continue innovating in digital production technology to create top-quality products at the lowest cost and in the shortest time."