For example, in the short waveguides increasingly utilized in silicon photonics devices, the input and output couplings can steer each other. As one side is optimized, the other shifts slightly and needs re-optimization. Formerly, this necessitated a time-consuming, serial sequence of back-and-forth adjustments of the input, then the output, repeating until a global consensus alignment was eventually achieved. Similarly, when optimizing an angle, the transverse alignment would be impacted and would conventionally need to be re-optimized, again in a time-consuming serial loop.
But with FMPA, these interacting alignments can often be optimized simultaneously, in parallel. This allows a global consensus alignment to be achieved in one go. Tracking and continuous optimization of all the alignments is also possible in many circumstances, allowing compensation of drift, curing stresses, and so on.
The results are much higher production throughput and often dramatically lower costs. As devices become more complex and precise, and as their production and test requirements grow more demanding, this parallelism is increasingly critical to process economics.