UPDATED. 2020-10-22 19:09 (목)
KLA launches new AI-applied wafer-level packaging systems
KLA launches new AI-applied wafer-level packaging systems
  • Nari Lee
  • 승인 2020.09.23 11:59
  • 댓글 0
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They will help with yield rate, quality
Image: KLA
Image: KLA

KLA has launched a semiconductor packaging inspection systems with artificial intelligence (AI) applied, the company said,

They are the Kronos 1190 wafer-level packaging inspection system, the ICOS F160XP die sorting and inspection system and the next generation of the ICOS T3/T7 Series of packaged integrated circuit (IC) component inspection and metrology systems. 

Their increased sensitivity and throughput as well as enhanced algorithms address the complexities of shrinking feature sizes, 3D structures and heterogeneous integration.

Customers will be able to improve device performance without relying on scaling silicon design nodes, KLA said.

The Kronos 1190 wafer inspection system utilizes high-resolution optics to provide inline process control for advanced wafer-level packaging process steps as features shrink and patterns become denser, the company said. The newly introduced DesignWise system integrates AI that ensures ensure that defects are correctly identified and sorted for superior quality control and yield learning.

The ICOS F160XP system performs inspection and die sorting after wafer-level packages are tested and diced. 

High-end packages, such as those used for mobile applications, can suffer from dicing induced laser-groove, hairline and sidewall cracks due to their brittle materials. These cracks are not detectable with traditional visual inspection, the company said.

The ICOS F160XP system includes the new IR2.0 inspection module, which combines optical and true IR side inspection, doubling the throughput for 100% IR inspection compared to the previous generation. 

The ICOS T3/T7 Series features several new configurations of fully automated optical IC component inspectors designed to address varying inspection needs throughout the packaging assembly process. They utilizes AI systems with deep learning algorithms to enable smarter binning of defect types, providing accurate feedback on package quality to sort good and bad parts with less operator review across a range of device types and sizes, KLA said.
 


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