A unique challenge has emerged in the Channel Hole process module of advanced 3D NAND manufacturing: control of the lateral silicon nitride recess post Channel Hole etch. A novel mid-infrared critical dimension (IRCD) metrology has been developed on a platform suitable for fab production. Compared traditional optical critical dimension (OCD) technology based on ultraviolet, visible, and near-IR light, the IRCD system exploits unique optical properties of common semiconductor fab materials in the mid-IR to enable accurate measurements of high-aspect-ratio (HAR) etches with high Z dimensional fidelity. Utilizing the mid-IR wavelength range, a robust and unique measurement methodology is demonstrated to measure the lateral silicon nitride recess that occurs post channel hole etch due to etch bias between silicon dioxide and silicon nitride. IRCD metrology is proven to have higher unique sensitivity for lateral silicon nitride recess than other inline non-destructive metrology techniques.
A novel mid-infrared critical dimension (IRCD) metrology has been developed on a platform suitable for fab production. Compared to traditional optical critical dimension (OCD) technology based on ultraviolet, visible, and near-IR light, the IRCD system exploits unique optical properties of common semiconductor fab materials in the mid-infrared to enable accurate measurements of high-aspect-ratio etched features. In this paper, we will show two examples of critical dry etch steps in 3D NAND channel formation module of an advanced node that require nondestructive process control: (1) channel hole active area etch and (2) amorphous carbon hardmask etch. In the first example, we take advantage of the absorption bands of silicon dioxide and silicon nitride to get accurate CD measurements at different depths, resulting in high-fidelity z-profile metrology of the channel – key to guiding process development and accelerated learning for 3D NAND device manufacturing. In the second example, the most common amorphous carbon hardmask materials for advanced 3D NAND nodes are opaque in the traditional OCD wavelength range; however, in the mid-infrared, there is light penetration and hence spectral sensitivity to dimensional parameters including sub-surface features. We show successful detection of intentional process skews and as well accurate bottom CD measurements of the hardmask.
With the aggressive scaling of semiconductor devices, the increasing complexity of device structure coupled with tighter metrology error budget has driven up Optical Critical Dimension (OCD) time to solution to a critical point. Machine Learning (ML), thanks to its extremely fast turnaround, has been successfully applied in OCD metrology as an alternative solution to the conventional physical modeling. However, expensive and limited reference data or labeled data set necessary for ML to learn from often leads to under- or overlearning, limiting its wide adoption. In this paper, we explore techniques that utilize process information to supplement reference data and synergizing physical modeling with ML to prevent under- or overlearning. These techniques have been demonstrated to help overcome the constraint of limited reference data with use cases in challenging OCD metrology for advanced semiconductor nodes.
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