Research On OPC And Lavout Hotspot Management

In the last half a century, the development of IC has been keeping its pace with Moore’s Law. Although the constantly increasing integration level of IC and the smaller and smaller critical dimension (CD) have brought daunting challenges to IC manufacturing, fortunately Moore’s Law is still valid because of the development of science and technology and people’s efforts. Resolution Enhancement Techniques (RETs) which make the available minimal CD nowadays smaller than1/6of the wavelength of illumination system play an important role in IC manufacturing under sub-wavelength lithography condition. Model-based Optical Proximity Correction (MBOPC) is one of the widely used RETs. It compensates pattern distortion during lithography by modifying mask patterns. As the technology node moves forward, hotspot management, one of Design for Manufacturability (DFM) techniques, gets more and more attentions. It provides another communication method between IC designer and manufacturer besides Design Rule Check (DRC). The work of this thesis focus on MBOPC and hotspot management.On MBOPC, three improved OPC methods are proposed in this thesis.A sparse matrix MBOPC algorithm. After fragmentation, we find out the "sensitive area" using lithographic model kernels. When the Jacobian matrix used in the matrix MBOPC is evaluated, only the elements that correspond to the segments in the sensitive area of every control site need to be calculated. The computations will be greatly reduced. This algorithm will speed up the matrix MBOPC process while maintaining high accuracy.An OPC method suitable for pixel array layout of image sensor. The layout of pixel array of CMOS image sensor is constructed by lining up the same pixel cell. The polygons in pixel cell layout are usually symmetric, therefore, symmetrical result is expected on the wafer. In the proposed method, we process the pixel array layout hierarchically, apply symmetrical fragmentation and symmetrical correction to the polygons. This method results in high correction speed and symmetrical OPC result. A fast OPC algorithm for1-D layout. For better lithography friendliness, the layout designed using1Dimensional Gridded Design Rules (1D GDRs) need to be optimized into dense lines pattern by line end extension, dummy insertion and gap distribution optimization. After optimization, the optimized wires coverage ratio to layout is much larger than the original wires. However, in the optimized layout, only the parts of original wires are the functional parts. With the proposed algorithm, during the model-based OPC process, most of the extended parts and dummies of the layout are excluded from correction with empirical fixed offsets to save run time, and only the functional parts of the layout and the extended parts adjacent to them will be corrected. This algorithm significantly improves the OPC speed. On hotspot management, a management flow and a hotspot classification method are discussed in this thesis.A hotspot management flow based on wafer inspection and pattern recognition. This flow includes following steps:defect filtering, hotspot classification, hotspot risk ranking, hotspot library building and hotspot pattern matching. It can analyze and manage wafer inspection result efficiently, and can provides informative feedback to the layout designer. The feedback will be used as guidance in layout design and modification to reduce defect and impove chip yield.Hotspot classification based on higher-order local autocorrelation and discrete2-D correlation. Hotspot classification is an important step of hotspot management. In the proposed method, firstly, we extract the features of the hotspot patterns using HLAC method. Secondly, the principal component analysis (PCA) is performed on the features for dimension reduction. Thirdly, the simplified low dimensional vector features of the hotspot patterns are used in the pre-clustering step. Finally, detailed clustering using pattern similarity calculated by discrete2-D correlation is carried out. The proposed method can classify hotspots under center-shifting condition effectively and speed up the classification process greatly.