Mask metrology is a key signoff step in photomask manufacturing process. The challenges for mask makers today are issues revolving around reliably measuring CD (Critical Dimensions) of features on device patterns and mask registration, as the complexity of the patterns increase. These challenges continue to grow on due to the increasing complexity of mask design and the shrinking dimensions of critical features. The CDs are typically measured by scanning electron microscope (CD-SEM). Registration deals with the accurate placement of features on the mask, this paper has a primary focus on mask metrology. To support the mask metrology needs mentioned above, CATS® worked with HOLON to generate an offline CDSEM recipe in both GUI/non-GUI mode for automatic measurement. This implementation leads to the customer being able to automate the process of metrology to effectively use CATS® with the HOLON ZX tool. CATS® integration allows for exhaustive tool commands that can be preset in the CATS® setup file to generate CDSEM recipe. CATS® validates the recipe generation to optimize ZX productivity. To support this flow, here is the outline and the necessary building blocks: - CATS® reads the Mask data (VSB, MEBES, JEOL) job deck with the device patterns, along with metrology locations (Measurements point file) to place the desired marks. - The setup (Job deck and Marks) can be saved as a CATS® Internal job deck format for better efficiency. - The flow terminates with the WRITEFILE step, translating the CATS® Job deck to Machine format files that are needed for automatic measurement on the Holon Tool (CSV file, OASIS, Bitmap files as per specification).
MPC has been a technology enabler since 32nm technology node, and the number of mask layers receiving MPC increases as technology node advances. Model-based Mask Process Correction (MB-MPC) has evolved from correction based on short-range Gaussian to full Machine Learning (ML) based model and correction. Model-based MPC has demonstrated efficacy in reducing mask error on advanced nodes, but often requires extensive computing resource to achieve the stringent mask fidelity and Critical Dimension (CD) requirements. On the other hand, rule-based Mask Process Correction (RB-MPC) has the advantage of fast turn-around time. This paper presents an approach to rule-based MPC that seeks to extract the maximum benefits of model-based MPC. The rules cover critical geometrical ‘building blocks’ such as lines, contacts, line-ends, notches. Derivation of the rules is guided by a mask process model. The goal of RB-MPC is to mitigate the long runtime of MB-MPC while minimizing loss in patterning fidelity. We will describe the methodology of rule derivation, implementation, and verification of RB-MPC. The RB-MPC approach meets accuracy requirements for 32-22nm technology nodes. For more advanced technology nodes, a hybrid RB-MB-MPC recipe is proposed to achieve both high accuracy and fast runtime.
Over the last few decades, the scope of MDP has evolved from a handful of simple tasks, to veritable “jack of all trades”, capable of an enormous array of functions, in several different operational scenarios. These functions range from machine-specific fractures, to Boolean operations, OPC, MPC, checking, and beyond, under various scenarios including specific hardware configurations (such as single CPU vs. cluster, memory size, type, and location), software configurations (including operating system, load balancing, and prioritization), and inputs and outputs (formats, sizes, and so forth). While this versatile capability of tools, such as Synopsys’ CATS software, is powerful, the expertise required to operate them efficiently keeping abreast of the changing requirements and capabilities, poses a significant challenge to the average user.
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