The evolution of 3D NAND memory devices is increasing the depth of HAR (High Aspect Ratio) hole structure. Consequently the technology to measure the shape of the structure is also becoming more difficult. In general, optical measurement method such as OCD (Optical Critical Dimension) is mainly used for measurement of the HAR structure, but optic technology has limitation in measurement of hole structure independently. To overcome this, SEM (Scanning Electron Microscopy) with high acceleration voltage of electron beam can be used for the measurement of bottom CD (Critical Dimension, diameter of a hole) of the hole structure. However this technology also has challenge in that the measured CD does not always represent the exact bottom CD of the structure. In order to solve this problem, we propose a method of inferring the actual depth where the measured CD is located by examining the change of the acceleration voltage and the angle of incident electron beam. The CDs of real product hole pattern were measured according to the change of landing energy of electron beam and the measured depth was calculated using proposed method. After inferring the CD measured from the actual hole structure, the method is verified in a sample having known structure figures. The proposed method can be used for 3D microstructure measurements using SEM technology in the future.
We developed imaging spectroscopic reflectometry (ISR) based on hyperspectral imaging and deep learning and built it as an in-line facility. After obtaining the reflectance as a hyperspectral cube of the 350 – 1100 nm wavelength region throughout the whole device wafer, dimension of the nanometer-sized structure can be imaged through the supervised learning model. In particular, by including near-IR region in the spectrum, the bottom critical dimension (BCD) of the high-aspect ratio structure such as channel hole (CHH) of the 3D NAND evaluated in this study can be imaged non-destructively and rapidly. After removing the top through decapsulation, the actual BCD was measured by SEM and was linked to the hyperspectral cube to construct a supervised learning model. The BCD predicted through ISR showed a correlation of R2=0.72 with the actual BCD. In addition, the shape of the defect on device chip caused by insufficient etch at the bottom of CHH, obtained by ISR was identical to the inspection image taken after decapsulation. Compared to spot measurement, ISR shows the advantage of being able to capture defects that occur at random locations in the device wafer. From our high volume sample of 3D NAND, ISR result showed a high correlation (R2 = 0.82) with the rate of failure caused by channel hole while the conventional spot measurement showed only R2 = 0.41. By using ISR, we could optimize the etching process for the wafer edge area where CHH formation is particularly weak.
As the measurability (sampling capacity, measurement coverage, and measurement speed) of metrology systems are being enhanced to keep pace with the evolution of semiconductor manufacturing processes, the detection of defective areas and hidden weak patterns by analyzing the massive measurement data is becoming significantly important. In this study, we propose new methods to detect defective areas and hidden weak patterns by mathematically processing massive measurement data. By applying the methods we propose, we were able to successfully detect the hidden weak signals of the millimeter scale in the wafer.
In case the process margin of the device is large and the defect tendency in the wafer occurs randomly, process monitoring were possible using limited sampling measurement values. Previous 3D structure metrology equipment (CD-SEM, ellipsometry, etc.) are not able to measure the entire structure of the wafer due to the speed limit. If the measurement location does not include a weak point, an error occurs in predicting the wafer defect rate. In this study, we propose a new method that can extract weak points from color maps obtained by high-speed inspection tools that can measure the entire wafer. We were able to reduce the process error by about 20% by weak point monitoring.
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