1. kurzweg@ece.drexel.edu, guezal@drexel.edu, skb25@drexel.edu Timothy P. Kurzweg, Allon Guez, and Shubham K. Bhat Drexel University Department of Electrical and Computer Engineering Knowledge Based Design of Optoelectronic Packaging and Assembly Automation

2. Motivation Current State-of-the-Art Photonic Automation Our Technique: Model Based Control Optical Modeling Techniques A System Level Example Conclusion and Future Work Overview

3. No standard for OE packaging and assembly automation. Misalignment between optical and geometric axes Packaging is critical to success or failure of optical microsystems 60-80 % cost is in packaging Automation is the key to high volume, low cost, and high consistency manufacturing ensuring performance, reliability, and quality. Motivation

4. + Arrayed Waveguide Optical Switch Optoelectronic Module Laser- Fiber www.bonders.com www.polytecpi.com OPTOELECTRONICSMECHANICS OptoMechatronics

5. Start Input Power Measurement & Loading Initial Throughput And Coarse Alignment Control And Optimization Bonding Post-Bond Testing Unloading End Manufacturing Process

6. Current State-of-the-Art LIMITATIONS: Multi-modal Functions Multi-Axes convergence Slow, expensive “Hill-Climbing” Technique Visual Inspect and Manual Alignment Initialization Loop Move to set point (X o ) Measure Power (P o ) Stop motion Fix Alignment Approximate Set Point=X o Assembly Alignment Task Parameters Off the shelf Motion Control (PID) (Servo Loop) Stop

7. Model Based Control ADVANTAGES: Support for Multi-modal Functions Technique is fast Cost-efficient Visual Inspect and Manual Alignment Initialization Loop Move to set point (X o ) Measure Power (P o ) Stop motion Fix Alignment Set Point=X o Learning Algorithm Model Parameter Adjustment Optical Power Propagation Model Correction to Model Parameter {X k }, {P k } FEED - FORWARD Off the shelf Motion Control (PID) (Servo Loop) Assembly Alignment Task Parameters

8. Model Based Control Theory KpKp KpKp P d (s) P r (s) + + + - R(s) E(s) If = P,

9. Optical Modeling Technique Use the Rayleigh-Sommerfeld Formulation to find a Power Distribution model at attachment point Solve using Angular Spectrum Technique – Accurate for optical Microsystems – Efficient for on-line computation Spatial DomainFourier Domain Spatial Domain

10. Inverse Model For Model Based control, we require an accurate inverse model of the power However, most transfer functions are not invertible Zeros at the right half plane Unstable systems Excess of poles over zeros of P Power distribution is non- monotonic (no 1-1 mapping) Find “equivalent” set of monotonic functions

11. Inverse Model: Our Approach Decompose complex waveform into Piece- Wise Linear (PWL) Segments Each segment valid in specified region Find an inverse model for each segment

12. Distance = 10um No. of. Peaks = 10 Edge Emitting Laser Coupled To a Fiber Aperture = 20um x 20um Fiber Core = 4 um Prop. Distance = 10 um Example: Laser Diode Coupling NEAR FIELD COUPLING

13. Feed Forward Set Point (Power) Feed Forward Current State-of-the-Art INCREASED SYSTEM PERFORMANCE OVER 18% Comparison of Methods Position @ Pmax= 12.6 um Power measured using a fiber detector of 4um core diameter

14. Nominal Model - K K + Proportional Gain Motor DynamicsPlant Model Derivative Desired Power Time Taken = 7 seconds Model Based Control System (1.41) + Inverse Model + + 20 18 16 14 12 Fiber Position (12.6 um) 1.5 1 0.5 0 1.5 1.3 1.1 0.9 0.7 Received Power (1.41 )

15. Model based control leads to better system performance Efficient optical modeling using the angular spectrum technique Inverse model determined with PWL segments Increased performance in example systems Hardware implementation Error prediction Learning Loop implementation Conclusions and Future Work