CMP slurry is primarily composed of abrasive particles, carrier liquids, and additives. During the CMP process, friction occurs between the abrasive particles and the wafer surface. The size and distribution of these particles significantly impact the planarization results. Oversized particles may damage the wafer surface, while undersized particles may be ineffective in achieving proper material removal. Additionally, particle dispersion plays a crucial role—uniform distribution is essential to prevent agglomeration. Ensuring that abrasive particles are within the optimal size range and evenly dispersed is key to achieving high-quality polishing performance.
Traditional signal-based methods typically assume that particles are perfectly spherical. However, in real-world scenarios, many particles deviate from this ideal shape. This discrepancy can lead to inaccuracies in particle size or scattering behavior, ultimately affecting the reliability of particle size distribution results.
In high-concentration slurries, signal interference between particles often occurs, causing data deviations and reducing accuracy. The presence of different media—such as bubbles or gels—can further disrupt optical signals. These anomalies cause significant signal distortion, leading to data that diverges substantially from actual values and, in some cases, becomes entirely unusable.
Such limitations make it difficult to investigate the root causes of issues within the process, hindering effective troubleshooting and quality control.
Traditional particle sizing methods primarily focus on measuring particle size and count—functions that are already widely available in many existing products. However, the AI Particle Imager goes beyond basic measurements by enabling rapid detection of microcontaminants in liquid samples. Leveraging advanced optical technology and algorithms, it can quickly and accurately capture and analyze the contents of liquid samples.
With high-level AI image recognition, the system is capable of identifying and classifying microcontaminants—such as crystals, gels, bubbles, dust, or fibers—helping to pinpoint the source of contamination and address the root cause of the issue.
For users who have long relied on manual sampling and analysis, AI technology offers automation and the ability to efficiently process large volumes of data. This significantly reduces labor costs while dramatically improving data accuracy.
| Comparison Categories | Traditional Signal Analysis | AI Image Analysis |
| Analysis Method | Light signal measurement | Image recognition + AI analysis |
| Shape Accuracy | Limited by spherical assumption | Measures roundness values |
| Medium Interference | Susceptible to bubbles/gel | Classifiable Foreign Particles |
| Accuracy | Potential bias | Higher precision |
Solution: LEADquid S Series – Intelligent Optical Imaging System for Fluids
This solution enables precise inspection of the particle size and distribution of powder raw materials, both before and after grinding. In addition to size measurement, image-based data analysis is used to evaluate the shape of abrasive particles. Ideally, abrasive particles should be round or near-spherical in shape to ensure consistent polishing performance and minimize wafer surface defects or unevenness caused by irregular shapes or excessive wear.
Using the AI Particle Imager, slurry samples in the market were tested and analyzed in layers at different stages: before grinding, after one grinding cycle, and after two grinding cycles. The particle size distribution charts show that after two cycles of grinding, the particle sizes stabilize and approach the optimal abrasive particle standards. This process helps customers ensure that their production process consistently meets quality standards.
