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A three-sided pyramid prism is used as a wavefront sensor in non-modulating mode with an artificial neural network (NN) as a control unit in an adaptive optical setup. The NN is composed of a convolution network for feature extraction from the pyramid signal and a three-layer feed-forward back-propagation net. The network is trained with over 15,000 examples of signal–wavefront pairs. Presenting wavefronts of 6 mm pupil diameter and a RMS error of under 200 nm to the adaptive optical system with the trained network, it can correct the aberrations within three loops to a wavefront with

We describe existing concepts for CMOS3-based Hartmann-Shack wavefront sensors and propose the next step for a further miniaturization of adaptive optics by integrating the modal wavefront reconstruction on chip. In conventional Hartmann-Shack wavefront sensing a CCD4 camera is placed on the focal plane of a microlens array. The spot pattern in the focal plane is captured, analyzed by image processing and the wavefront is often decomposed into orthogonal modes, usually expressed in coe.cients of Zernike polynomials. The single-chip modal wavefront reconstructor would include all these processing steps in a single chip. We propose to use already existing CMOS-based wavefront sensor concepts and add a hardware arti.cial neural network for modal wavefront reconstruction.