I worked a bit on the extraction process so I can chime in here a bit. The first part is to just mark the x,y locations of where all the bits are, generally by the intersection of the rows and columns of the microcode array.

Then you have to classify them as 0's or 1's. Each is visually distinct, a 1 being encoded by the presence of a transistor and a gap in the polysilicon. We didn't have to guess which is which is by the nature of Intel microcode we could assume 0's were much more frequent, so a transistor meant a 1.

There are some automatic tools designed to perform this work via color thresholding, but they didn't work very well here because some of the mosaic was blurry, and a lot of dust had crept in which created false 1 bits.

Instead, we trained a convolutional neural network to classify the extracted bit regions into 0's and 1's. This was overlaid back onto the original mosaic as white or black squares at 50% opacity.

Then we spent several long, tedious days just checking the results for errors. Finally we had the raw 2d array of bits - the next step is to extract the microcode words from the bit array.