Automatic Optimal Command for the Linearization of the CMUT Output
Résumé
Capacitive micromachined ultrasonic transducer (cMUT) is a promising alternative to piezoelectric transducers. However, its nonlinear behavior could limit its use in medical ultrasound technology, mainly for therapy and non-linear imaging. Several linearization methods based on a precompensation applied to the input voltage have been proposed to cancel out the harmonic components of the cMUT output signal. An approach based on the principle of the Wiener filter has first been proposed. A second technique empirically adds a third harmonic component 3f0 to the useful input signal centered at the frequency f0 in order to cancel out only the second harmonic. Nevertheless, these techniques are efficient while the cMUT works in narrowband operating conditions only. A third method based its analysis on an equivalent non-linear electro-acoustic scheme of the cMUT. From this scheme, the determination of the optimal “input” signal able to suppress harmonic components is then straightforward. Operating conditions are here not restricted but the non-linear model applies to a single cell only, without considering a population of cMUTs, as in the case of one element of array. Finally, Satir and Degertekin have proposed to add a passive component in series with the cMUT in order to convert the input voltage control into an input charge control. However, none of these methods is entirely satisfactory, because they require a priori knowledge and no method is automatic. In this paper, we proposed a novel method to linearize the signal transmitted by cMUT. The method aims to automatically provide an optimal input signal eliminating the nonlinear component of the cMUT output. It consists in choosing first a target signal, with the desired spectrum or temporal shape. This target signal is used as the input excitation signal of the cMUT but after being filtered with a nonlinear autoregressive filter. The output of this filter is the optimal signal and its parameters describe the optimal command. A simplex algorithm is used for seeking the filter parameters. The method was tested and validated with simulations and experiments, using spectral and temporal targets. The results have shown that the nonlinear components were reduced of 97% in comparison with the case without compensation.
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