A set of design for manufacturing (DFM) techniques have been developed and applied to 45nm, 32nm and 28nm logic process technologies. A noble technology combined a number of potential confliction of DFM techniques into a comprehensive solution. These techniques work in three phases for design optimization and one phase for silicon diagnostics. In the DFM prevention phase, foundation IP such as standard cells, IO, and memory and P&R tech file are optimized. In the DFM solution phase, which happens during ECO step, auto fixing of process weak patterns and advanced RC extraction are performed. In the DFM polishing phase, post-layout tuning is done to improve manufacturability. DFM analysis enables prioritization of random and systematic failures. The DFM technique presented in this paper has been silicon-proven with three successful tape-outs in Samsung 32nm processes; about 5% improvement in yield was achieved without any notable side effects. Visual inspection of silicon also confirmed the positive effect of the DFM techniques.

Photolithography for the formerly "non-critical" implant blocking layers is becoming more challenging as edge placement control budgets for junction definition shrink with each node. In addition to the traditional proximity effects associated with the implant layer mask, the underlying active and gate layers can interact through a variety of mechanisms to influence the edge placement of the developed implant layer. These mechanisms include bulk reflectivity differences, resist thickness thin film interference effects, reflective notching from pattern sidewalls, reflections from curved surfaces, focus differences, and more. While the use of organic developable bottom antireflection coating (dBARC) can be effective in minimizing these influences, it does represent an added complexity and cost, and processes are still relatively immature. Without such a dBARC, the CD variation due to underlying layers can easily exceed 50 nm, or more than 25% of the target dimension. We propose here a framework for modeling and correcting for these underlayer effects. The approach is based upon calibration of an optical model representing only implant mask proximity effects and two additional optical models which represent the effects of the underlayer topography. Such an approach can be effective in delivering much improved CD control for complex layouts, and represents only a small impact to full-chip correction runtime.

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Semiconductor manufacturing’s full chip RET/OPC operations rely on the process models calibrated against metrology data collected from custom designed test structures. Physics-based compact models and machine learning models inherently carry the issue of model coverage often synonymous with calibration test pattern coverage. Therefore, process models frequently fail to predict unseen patterns within error tolerance. With the push for advanced technology node, such events can even occur after a node is declared HVM ready. Foundries have been combating the model coverage deficiency through costly model revisions, or expensive repair flows. There has always been the desire to have capability to screen and enhance compact model of potential coverage issue. In this paper, we use the machine learning clustering platform to learn the signatures of the model calibration test patterns and then compare them to the new design patterns in terms of feature vectors’ space correlated to model parameters’ space. The comparison provides not only the locations of the new patterns but also the similarity ranking with respect to the reference pattern, so that those patterns can be included and be further analyzed for better model coverage. These patterns are often suitable candidates to be included into new model calibration set. In this application, full chip capability is also essential besides the accuracy of the learning. The full-chip pattern check needs to be done quickly and efficiently; hence this technology could be adopted for new chip screening, highlighting areas worth paying extra attention to during inspection.

Current metal integration process normally uses hard mask for dry etch process instead of resist to compensate thin resist thickness. As the pattern size becomes smaller, thinner resist thickness is required to get sufficient lithography process window. But this trend increases a risk of systematic hard defect like the metal line bridge in damascene process because of consumption in dielectric material during dry etch process.The sub-32nm patterning with the single exposure is almost on the edge with the 193nm immersion lithography. The smaller lithography CD makes the aerial image contrast worse, which means higher DC level in the unexposed area. This higher DC level, latent image, can sacrifice the resist thickness in the unexposed area and this recessed resist thickness is very harmful for the etch process with the current hard mask which may induce the metal line bridge.Although OPC verification step checks potential hot spot during mask type out flow, there is no predictable method to detect systematic potential defects described above. In this paper, we proposed a new method to detect such potential defects and discussed the performance with wafer result. With this predictable model based search method, the robust patterning process in the sub-32nm node can be developed.