Development of artificial neural networks for real time, in situ ellipsometry data reduction

Development of real time in situ monitoring and control of thin film depositions using ellipsometry requires both data acquisition and processing to be rapid. Present speeds of measurement and computation of basic parameters, Ψ and Δ, are sufficient for data acquisition which is essentially real time. However, computation of film parameters, such as thickness and optical properties, generally cannot keep up with the incoming data and must be performed in a batch mode after the deposition.

This work describes the development of enhanced, high speed data reduction algorithms using artificial neural networks (ANN). The networks are trained using computed data and subsequently give values of film parameters in the millisecond time regime. The ANN outputs are used as initial estimates in a variably damped least squares algorithm for accuracy improvement. The combination of these two algorithms provides very accurate solutions in 75 ms per point on a DEC VAX 8800 multiprocessor system running at a combined 12 Mips. This speed is suitable for real time film monitoring and control for growth rates up to 10 nm per second. Results for fixed angle of incidence, single wavelength, in situ data for Ni deposited on BK7 substrates are presented.