In semiconductor industry, fast and effective measurement of pattern variation has been key challenge for assuring massproduct quality. Pattern measurement techniques such as conventional CD-SEMs or Optical CDs have been extensively used, but these techniques are increasingly limited in terms of measurement throughput and time spent in modeling. In this paper we propose time effective pattern monitoring method through the direct spectrum-based approach. In this technique, a wavelength band sensitive to a specific pattern change is selected from spectroscopic ellipsometry signal scattered by pattern to be measured, and the amplitude and phase variation in the wavelength band are analyzed as a measurement index of the pattern change. This pattern change measurement technique is applied to several process steps and verified its applicability. Due to its fast and simple analysis, the methods can be adapted to the massive process variation monitoring maximizing measurement throughput.
In this paper we proposed a new semiconductor quality monitoring methodology – Process Sensor Log Analysis (PSLA) – using process sensor data for the detection of wafer defectivity and quality monitoring. We developed exclusive key parameter selection algorithm and user friendly system which is able to handle large amount of big data very effectively. Several production wafers were selected and analyzed based on the risk analysis of process driven defects, for example alignment quality of process layers. Thickness of spin-coated material can be measured using PSLA without conventional metrology process. In addition, chip yield impact was verified by matching key parameter changes with electrical die sort (EDS) fail maps at the end of the production step. From this work, we were able to determine that process robustness and product yields could be improved by monitoring the key factors in the process big data.
In the era of sub-30nm devices, the size of the defects on semiconductor wafer has already exceeded the resolution limit
of optic microscope, but we still can't help using optical inspection tools. Therefore, the contrast enhancement technique
is more useful rather than the resolution itself. The best contrast can be taken by the optimized light conditions such as
wavelength, polarization, incidence angle and so on. However these kinds of parameters are not easily estimated
intuitively because they are strongly dependent on the pattern structures and materials. In this paper, we propose a
simulation methodology to find those optic conditions to detect sub 20nm defect. The simulation is based on FDTD
(Finite Difference Time Domain) calculation and Fourier optics.
The shrinking of design rule requires the short wavelength light used in the optical inspection system. However, the
existence of the condition that the long wavelength light becomes effective for the defect detection in line/space structure
is known. Calculation results using numerical simulation showed that the probe light can penetrate to the line/space
structure depending on the polarization even though the light has long wavelength. A new model was introduced to make
theoretical explanation of this abnormal behavior of long wavelength light and the mechanism of optical penetration was
clarified. In this model, the averaged extinction coefficient was calculated in consideration of the wavelength and the
period of the line/space structure. Using this model, the transmittance was calculated and compared with simulations.
The fact that the calculation result is agreed with the simulations showed this model's utility. This result shows that the
probe light can reach to the bottom of line/space structure in the inspection system for semiconductor devices even
though the light has long wavelength. It means that the long wavelength light can be used effectively for the defect
detection of the micro periodic structure in the semiconductor inspection system.