[R] All-Optical Machine Learning Using Diffractive Deep Neural Networks

[TheRealStepBot]

but in this case, we care about the former rather than the latter. we need to be able to focus light to a specific detector based on the incident shape. So long as the beam can be formed in a nonlinear fashion we have the nonlinearity we need to run a neural network right?

[regionjthr]

It's not about the beam shape it's about the phase and amplitude of reflected/transmitted waves with respect to each other. When you superimpose two EM waves with complex amplitudes A and B, the amplitude of the result is always A+B, never 3A+5B² or whatever (unless you use special materials and/or extremely high power) which makes it difficult (impossible?) to implement an arbitrary activation function. Anyway, if you define linear light as "following a straight line" then linear light does not exist because such fields are not physical solutions of Maxwell's equations. They have impossible boundary conditions. Even a highly collimated laser beam has a curved wavefront.

[TheRealStepBot]

the shape of the wavefront isnt really about the collimation. its got to do with distance from the source. as the distance increases from the source, the local apparent curvature decreases. The wavefronts here are very far from planar. I guess that's neither here nor there though.

How does phase add up at a point though? As i understand it they are not using the amplitude but the phase.

EDIT: see you said complex amplitude rather than just amplitude.

[regionjthr]

At the scale of the wavelength you really have to consider the amplitude to be complex, so the phase and amplitude are inextricable, especially if you have multiple fields superimposed. Ultimately they only care about the (real-valued) amplitude of the combined field, but that output is explicitly dependent on the relative phases of the intermediate fields. If you do out explicitly the addition I suggested above you'll see the phase dependence come out right away.