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The manufacturing of next generation semiconductor devices forces metrology tool providers for an exceptional effort in order to meet the requirements for precision, accuracy and throughput stated in the ITRS. In the past years hybrid metrology (based on data fusion theories) has been investigated as a new methodology for advanced metrology [1][2][3]. This paper provides a new point of view of data fusion for metrology through some experiments and simulations. The techniques are presented concretely in terms of equations to be solved. The first point of view is High Level Fusion which is the use of simple numbers with their associated uncertainty postprocessed by tools. In this paper, it is divided into two stages: one for calibration to reach accuracy, the second to reach precision thanks to Bayesian Fusion. From our perspective, the first stage is mandatory before applying the second stage which is commonly presented [1]. However a reference metrology system is necessary for this fusion. So, precision can be improved if and only if the tools to be fused are perfectly matched at least for some parameters. We provide a methodology similar to a multidimensional TMU able to perform this matching exercise. It is demonstrated on a 28 nm node backend lithography case. The second point of view is Deep Level Fusion which works on the contrary with raw data and their combination. In the approach presented here, the analysis of each raw data is based on a parametric model and connections between the parameters of each tool. In order to allow OCD/SEM Deep Level Fusion, a SEM Compact Model derived from [4] has been developed and compared to AFM. As far as we know, this is the first time such techniques have been coupled at Deep Level. A numerical study on the case of a simple stack for lithography is performed. We show strict equivalence of Deep Level Fusion and High Level Fusion when tools are sensitive and models are perfect. When one of the tools can be considered as a reference and the second is biased, High Level Fusion is far superior to standard Deep Level Fusion. Otherwise, only the second stage of High Level Fusion is possible (Bayesian Fusion) and do not provide substantial advantage. Finally, when OCD is equipped with methods for bias detection [5], Deep Level Fusion outclasses the two-stage High Level Fusion and will benefit to the industry for most advanced nodes production.
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