Through techniques such as ILT, curvilinear designs and their associated masks have demonstrated benefits over Manhattan type for delivering superior wafer lithography process latitude. Moreover, a number of native design applications such as silicon photonic IC and curvilinear interconnect require delivery of masks with non-Manhattan geometries. Consequently, as enabled by the use of multi-beam mask writers (MBMW), we see the adoption of curvilinear masks in production to grow steadily. One of the more challenging topics for curvilinear adoption is on determining the optimum tradeoff between mask manufacturability and wafer imaging. To maximize the benefits of curvilinear masks without incurring an undue impact from mask complexity, it is beneficial to develop optimized layout validation checks such as MRC which can be implemented to achieve an optimum tradeoff. We will present a methodology to perform curvilinear mask manufacturability optimization using a specially designed set of parametric curvilinear test patterns. The techniques are demonstrated in support of a DRAM implementation study where ILT is applied to improve the wafer performance of a contact type layer. We describe a parametric test chip covering curvature, width, space and area and the mask data generated is applied to evaluate different curvilinear layout constructs and correlations between mask manufacturability and simulated wafer performance. We revisit the question on whether ILT actually leads to relaxed MRC constraints compared to Manhattan designs for the same design application. In addition, advanced mask characterization techniques such as 2D contouring are applied to consider the limitations of purely geometrical rule checking versus a full model based approach that can consider mask pattern fidelity in ILT layout generation.
For advanced technology nodes, it’s critical to utilize resolution enhancement technique (RET) methods to improve pattern fidelity and wafer yield. Conventional techniques including rule-based SRAF (RB-SRAF) and model-based SRAF (MBSRAF) methods have been widely adopted to increase the manufacturing process window. ILT delivers superior imaging performance compared to both RB-SRAF and MB-SRAF methods, at the expense of slower performance and more inconsistency issue. Recent advancement of machine learning techniques opens up new gateways for more RET enhancements by overcoming these challenges, thus providing a pathway to extend ILT solution to full chip design. In this paper, we developed an end-to-end flow that seamlessly incorporated model training and application for full chip ILT MBSRAF generation and optimization via POLY-GAN, a new Generative Adversarial network (GAN) geared for fast, in-context and accurate ILT MB-SRAF synthesis. An image based deep learning architecture similar to pix2pix conditional GAN was utilized in our study. In this paper, we demonstrate that ML based full chip ILT MBSRAF generation yields superior process window compared to rule based SRAF generation, while maintaining comparable run-time performance.
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