Intelligent Motion Control: How Active Alignment Technology Speeds Up Manufacturing of Optical Systems

Examples such as these abound.  All involve a figure-of-merit which reaches a peak at the desired position and orientation.  And similar bounties of process-cost reduction are seen when conventional micro positioning is supplanted by these new intelligent optimization engines combined with high quality industrial multi-axis positioning mechanisms.  Furthermore, the pioneering parallelism that benefited Silicon Photonics production economics so dramatically—the ability to simultaneously align multiple photonic elements across multiple channels and through multiple degrees of freedom in one step—also applies broadly to the general case of industrial precision assembly.

Enabling intelligence by enabling parallelism

The key is how the parallel optimization can replace time-consuming loops.  For example, a gimbaling optimization of a lens in theta-X and theta-Y formerly needed to periodically halt so that the transverse alignment in XY could be corrected, and then the theta-X/theta-Y optimization could start over again, and on and on in a lengthy loop until a global consensus optimum is achieved.  Now, both optimizations can proceed simultaneously, yielding substantially faster global optimization and greatly reducing process costs.

We see now that this is broadly applicable to many manufacturing fields.  All that is needed is for the optimization controller to directly receive an adequately fast figure-of-merit, and the optimization can then begin across all the involved channels and degrees-of-freedom.  In familiar Silicon Photonics applications, the quantity being optimized is optical throughput, meaning power.  So the signal conveyed to the controller is typically the output of a high-bandwidth optical power meter or transimpedance amplifier.  That is all the controller needs to intelligently determine the optimal position and orientations of the elements it is positioning.

Returning to the new applications examples cited above, it’s easy to see how this capability maps to these new applications.  In the case of a laser cavity, the mutual orientation of reflectors, gratings and other constituents similarly must be optimized, and the figure of merit is optical output.  Dependencies between elements and geometrical dependencies for each element can be unwrapped automatically through the parallelized algorithm.  In the case of a multi-element camera such as the billions of increasingly sophisticated smartphone cameras assembled each year, the metric of image quality can be a straightforward and fast calculation of image sharpness, such as a 2D FFT or modulation transfer function calculation.  When conveyed to the controller at a high rate, this can similarly drive the simultaneous optimization of elements across multiple degrees of freedom, reducing or eliminating the need for those time-consuming process loops.

Optimization is universal

Key to all this is the fact that most figures-of-merit are substantially unimodal peaking functions near optimum, meaning they exhibit a hill-shaped profile that rises and then falls as the orientation of each element is exercised in each of its degrees of freedom.  The same internalized mathematical algorithms that achieved the radical capability of simultaneous optimization in Silicon Photonics production applies in these additional applications as well.  At the root of this capability is the novel, parallelized digital gradient search, a specialized category of gradient ascent algorithm that itself is a relative of the Euler-Lagrange equation.  This is a highly generalized technology, hence its broad applicability.  Importantly, a model of the coupling and dependencies need not be known, nor must the application be predictable or tightly reproducible.

Enabling a precipitous decline in production cost

What this means for production economics across these new fields is still unfolding, but the promise is similar to what we have seen in Silicon Photonics process automation.  Fields as diverse as LIDAR, lasers, life sciences, data storage, quantum computing—virtually any field where production economics are impacted by lengthy process loops in pursuit of a global consensus optimization—will benefit.

Which brings us back to that high-altitude view.  We witness a foundational shift in positioning technology, from its advancing history of positioners ever more precisely doing what they told, to do to a new paradigm of truly intelligent positioning.  The era dawns where positioners can now perform autonomous optimization of process quantities, and the consequences for industry will be broad and deep.

Author: Scott Jordan