  Fourier phase retrieval is essential for high-definition imaging of nanoscale
structures across diverse fields, notably coherent diffraction imaging. This
study presents the Single impliCit neurAl Network (SCAN), a tool built upon
coordinate neural networks meticulously designed for enhanced phase retrieval
performance. Remedying the drawbacks of conventional iterative methods which
are easiliy trapped into local minimum solutions and sensitive to noise, SCAN
adeptly connects object coordinates to their amplitude and phase within a
unified network in an unsupervised manner. While many existing methods
primarily use Fourier magnitude in their loss function, our approach
incorporates both the predicted magnitude and phase, enhancing retrieval
accuracy. Comprehensive tests validate SCAN's superiority over traditional and
other deep learning models regarding accuracy and noise robustness. We also
demonstrate that SCAN excels in the ptychography setting.
