The benefits of hyper-profile and MGML in the DRAM storage node application.
DRAM storage node profile measurement during high aspect ratio (HAR) etch has been one of the most challenging metrology steps. DRAM storage node profile affects refresh time and device electric quality. So, controlling this profile is one of the key challenges. Conventional 3D modeling in Optical Critical Dimension (OCD) metrology has typically used multiple cylinder stacks. This method cannot provide an accurate model and computed spectrum through the RCWA engine. This means we need a more accurate model. In this paper, we used hyper-profile to accurately measure a hole profile for better process control. Hyper-profile uses a polynomial to describe the smooth shape of a hole profile, which is much closer to the real product and provides a more accurate computed spectrum. With hyper -profile, a continuous storage node hole profile and managed CD correlation are achieved. It can maintain the same profile complexity with less degree of freedom, reducing the model uncertainty and ensuring more robust regression. On the other hand, as the metrology error budget becomes stricter and the process variation cycle is increasing, the OCD-based model-guided machine learning (MGML) approach can provide a faster solution turnaround time with more accurate measurements than either pure OCD or pure ML approaches. It also can better decorrelate profile CDs and achieve more robust profile monitoring. In this paper, we will demonstrate the above benefits of hyper-profile and MGML in the DRAM storage node application.
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