Robust Defect Detection System Using Double Reference Image Averaging for High Throughput SEM Inspection Tool

This paper reports a defect detection system for a high throughput SEM inspection tool. Although the system has a big advantage compared to optical tools, that is, the ability to detect smaller defects and voltage contrast defects, the cost of ownership (COO) remains high. To enhance COO, throughput enhancement is the critical issue. A larger beam current results in lower image noise and higher throughput. At the same time, the larger the beam current, the lower is the resolution. We suggest a robust defect detection system as a solution to the trade-off between resolution and throughput. The main inspection targets are the voltage contrast (VC) defects on the memory matte. The system judges defects by subtracting a detected image from a reference image, and then determining the defective portion as a larger difference than the pre-determined threshold in the subtracted image. If the noise variation for the two images is a in both cases, the noise in the subtracted image is 1.4 sigma (= radic(sigma² + sigma²)). We have developed a double reference image averaging (DRIA) system which improves the noise in the reference image by averaging repetitive patterns on the memory matte and noise variation on subtracted image is enhanced to a sigma (= radic(sigma² + sigma² )) ideally. This enhancement is equivalent to a two times higher throughput than conventional systems. We also improved the electron beam optics and show that our system throughput is 400 Mpixels per second (pps), which is four times faster than previous systems