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Ultra-thin layers of aluminum oxide (less than 1 nm) were grown by atomic layer deposition (ALD) technique on hydrogen-terminated silicon substrates. A new technique, called "plasma defect etching", was proposed for the continuity evaluation of such a layer. The layer was examined by using it as a mask in silicon etching at cryogenic temperatures in a DRIE reactor. The etch profile was characterized by scanning electron microscopy (SEM) and atomic force microscopy (AFM) techniques. Island formation during initial cycles was confirmed. Thicker aluminum oxide layers (1-5 nm thick) were patterned by wet or dry (plasma) etching and used as a mask for deep silicon etching at cryogenic temperatures in a DRIE reactor, using SF 6 and O 2 gas mixture. We found aluminum oxide to be an extremely resistant mask, etched only 0.05 nm/min. The value for the Si to Al 2 O 3 selectivity reached 70 000:1.

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I Publication I J. Tiilikainen, J.-M. Tilli, V. Bosund, M. Mattila, T. Hakkarainen, V.-M. Airaksinen and H. Lipsanen, Nonlinear fitness-space-structure adaptation and principal component analysis in genetic algorithms: an application to x-ray reflectivity anal-Abstract Two novel genetic algorithms implementing principal component analysis and an adaptive nonlinear fitness-space-structure technique are presented and compared with conventional algorithms in x-ray reflectivity analysis.Principal component analysis based on Hessian or interparameter covariance matrices is used to rotate a coordinate frame. The nonlinear adaptation applies nonlinear estimates to reshape the probability distribution of the trial parameters. The simulated x-ray reflectivity of a realistic model of a periodic nanolaminate structure was used as a test case for the fitting algorithms. The novel methods had significantly faster convergence and less stagnation than conventional non-adaptive genetic algorithms. The covariance approach needs no additional curve calculations compared with conventional methods, and it had better convergence properties than the computationally expensive Hessian approach. These new algorithms can also be applied to other fitting problems where tight interparameter dependence is present.

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