A new NbN multilayer technology has been developed on 3 inch diameter R-plane sapphire substrates, for combining on-chip fast RSFQ circuits with GHz bandwidth optical links. The circuits take advantage of two high quality (110) NbN layers sputtered epitaxially on sapphire at 600°C and selectively patterned: a 400 nm thick layer (LL-250 nm at 6K) acts for the ground-plane and microbridge photodetectors are made of a 3.5-8 nm thick NbN epilayer with T, above 11 K. Innovative dielectrics formed of 10 nm thick MgO sputtered on top of 200 nm S O z layers are found to improve significantly the superconductivity of NbN junction electrode lines deposited below 300°C. Good quality, hysteretic 2 pm2 area, NbN/MgO/NbN junctions with high J, (up to 50 kA/cm2) are obtained with very large gap voltage (6.20 mV) and low sub-gap leakage current (V,,, > 15 mV) at 4.2 K. At 11 K such junctions are found self-shunted (J,-10 kA/cm2) with RJ, above 0.5 mV and with low J, spread in arrays. J, can be adjusted (reduced) without any detrimental effect on the junction quality or spread by annealing at 250°C.

International audienceThe semiconductor manufacturing industry has a large-volume multistage manufacturing system. To insure the high stability and the production yield on-line a reliable wafer monitoring is required. The approach, called Virtual Metrology (VM) is defined as the prediction of metrology variables (either measurable or non measurable) using process and wafer state information. It consists in the definition and the application of some predictive and corrective models for metrology outputs (physical measurements) in function of the previous metrology outputs and of the equipment parameters of current and previous steps of fabrication. The goals of this paper are to present a methodology for VM module for individual process applications in semiconductor manufacturing and to present a case study based on industrial data

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The semiconductor industry is continuously facing four main challenges in film characterization techniques: accuracy, speed, throughput and flexibility. Virtual Metrology (VM), defined as the prediction of metrology variables using process and wafer state information, is able to successfully address these four challenges. VM is understood as definition and application of predictive and corrective mathematical models to specify metrology outputs (physical measurements). These statistical models are based on metrology data and equipment parameters. The objective of this study is to develop a model predicting the CVD oxide thickness (average) for an IMD (Inter Metal Dielectric) deposition process using FDC data (Fault Detection and Classification) and metrology data. In this paper, two VM models are studied: one based on Partial Least Squares Regression (PLS) and one based on Tree ensembles. We will demonstrate that both models show good predictive strength. Finally, we will highlight that model update is key for ensuring a good model robustness over time and that an indicator of confidence of the predicted values is necessary too if the VM model has to be use on-line in a production environment.

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