Source mask optimization (SMO) emerged as powerful resolution enhancement technique (RET) for advanced technology nodes. However, there is a plethora of flow and verification metrics in the field, confounding the end user of the technique.

2012年3月1日 · With the source mask optimization (SMO) methodology, the lithography process window can be improved and a smaller mask error enhancement factor (MEEF) can be achieved. In this paper, the Tachyon...

Source mask optimization (SMO) is one of the indispensable resolution enhancement techniques to guarantee the image fidelity and process robustness for the 2Xnm technology node and beyond. The optimization capacity and convergence efficiency of SMO are important, especially for full-chip SMO.

Source mask optimization (SMO), a primary resolution enhancement technology, is one of the most pivotal technologies for enhancing lithography imaging quality....

2016年3月15日 · In this paper, we describe a novel method to implement DDM in the SMO flow. The source sectorization is defined by following the universal pixel sizes used in SMO. Fast algorithms are developed to enable computation of edge signals from each fine pixel of …

2017年7月1日 · In this paper, we evaluate the Tachyon SMO flow with the N28 node to optimize an illumination source for full-chip mask tape-out with Tachyon OPC+. Resist profile aware SMO with pattern selection is adopted to gain both litho-performance and runtime reduction.

2011年10月13日 · The aim of this work is to discuss advantages of using a comprehensive novel SMO flow that outperforms conventional techniques in areas of high capacity simulations, resist modeling and the production of a final manufacturable mask.

2011年10月1日 · We show results that indicate the importance of adding large number of patterns to the SMO exploration space, as well as taking into account resist effects during the optimization process and how...

2012年6月29日 · Source mask optimization (SMO) emerged as powerful resolution enhancement technique (RET) for advanced technology nodes. However, there is a plethora of flow and verification metrics in the field, confounding the end user of the technique.

We show results that indicate the importance of adding large number of patterns to the SMO exploration space, as well as taking into account resist effects during the optimization process and how this flow incorporates the final mask as a production solution.