Yahoo Finance

• 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%

SEOUL, South Korea, Oct. 7, 2024 /PRNewswire/ -- Today, LG Innotek (CEO Moon Hyuksoo) announced the development and application of the industry's first "Artificial Intelligence (AI)-based inspection system for incoming raw materials", designed to detect defects at the point of receipt and prevent the use of substandard raw materials in the process.

LG Innotek applied its AI-based inspection technology, developed by combining material information and AI image processing technologies, to the RF-SiP (Radio Frequency System-in-Package) process. Recently, the technology was also introduced for the FC-BGA (Flip Chip Ball Grid Array), and is expected to further enhance the competitiveness and quality of LG Innotek's high-value semiconductor substrate products.

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.