Real System
Input
Output
+
Adjustment
Scheme

Error
Estimated
model
Model
Step 1: Assume system to be described as , where y is the output, u is the input and is the vector of all unknown parameters.
Step 2: A mathematical model with the same form, with different parameter values is used as a learning model such that
Step 3: The output error vector, e , is defined as .
Step 4: Manipulate such that the output is equal to zero.
= wavelength = 630 nm
k = wave number associated with the wavelength
a = centertocenter separation = 32 um
b = width of the slit = 18 um
z = distance of propagation =1000 um
and
Step5:
Itfollows that
Identification Flowchart
Example System
 The initial feedforward set point is obtained from the optical power modeling done in MATLAB.
 This set point is given as an input to the PMDI motion control software which follows a PID loop by measuring the power.
 We start with an initial guessed value of “a” which is the centertocenter separation between the slits.
 The inner PID loop is repeated 5 times after which the outer learning loop comes into effect.
 The learning loop updates the estimated set points and tracks the actual set point. In this simulation, the learning algorithm was run 28 times.
Manual
FiberFiber Alignment
SemiAutomatic
 No standard for OE packaging and assembly automation.
 Packaging is critical to success or failure of optical microsystems.
 6080 % cost of optical component/system is in packaging.
For the double slit aperture, the irradiance at any point in space is given as:
Automation is the key to high volume, low cost, and high consistency manufacturing ensuring performance, reliability, and quality.
Experimental Results
Theory and Simulation
We present the above optical system with one unknown( slit width “a”) exhibiting inputoutput differential equation
( m is unknown and K is known )
The variables u, y, and are to be measured
Optical setup
Amplifiers, Encoders
Interpolators, Motion Controller
{ }
Step 1:
and
{ Assume estimated model and }
Step 2:
The Sensitivity coefficients are contained in
where
Step 3:
Inaccurate modeling could lead to deviation from the actual values.
Power Meter readings
Learning Equation :
 Activated at a lower sampling frequency.
 Specific and appropriate tasks.
 Provides opportunities for the system to improve upon its power model.
 Adjust the accuracy on the basis of “experienced evidence.”
Visit www.ece.drexel.edu/opticslab/results/Automation.wmv to watch the Model Based Control Video
Visit www.ece.drexel.edu/opticslab/results/learning.wmv to watch the Learning Identification Video
We track “m” to be 1.86 which relates to a slit width “a” of 32um.
Criteria for choosing e
If e
is too large, the schemes will diverge.
Initial X Encoder Position = 10477
Final X Encoder Position = 10810
Initial Y Encoder position = 24
Final Y Encoder position = 25
If e is too small, then
will approach
very slowly.
Selection of a suitable e determined by a trial and error process.
Algorithm
 Comparison of Power Levels
 Current StateoftheArt = 0.644 uW
 Our Technique: Model Based Control = 1.55 uW
 Our Technique: Learning Identification Control = 1.675 uW
Thus, we show an increase in power level reached along with increased efficiency and accuracy.
 Shubham K. Bhat, T.P.Kurzweg, Allon Guez "Learning Identification of OptoElectronic Automation Systems", IEEE Journal of Special Topics in Quantum Electronics, May/June 2006.
 Shubham K. Bhat, T.P.Kurzweg, Allon Guez,” Simulation and Experimental Verification of Model Based OptoElectronic Packaging Automation”, International Conference on Optics and Optoelectronics Conference, Dehradun, India, December 1215, 2005.
 Shubham K. Bhat, T.P.Kurzweg, Allon Guez, “Advanced Packaging Automation for OptoElectronic Systems”, IEEE Lightwave Conference, New York, October 2004 .
 T.P. Kurzweg, A. Guez, S.K. Bhat, "Model Based OptoElectronic Packaging Automation," IEEE Journal of Special Topics in Quantum Electronics, Vol.10, No. 3, May/June 2004, pp.445454.