[email protected], [email protected], [email protected]
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
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
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
+Arrayed Waveguide
Optical Switch
Optoelectronic Module
Laser- Fiber
www.bonders.com
www.polytecpi.com
OPTOELECTRONICS MECHANICS
OptoMechatronics
Start
Input Power Measurement &
Loading
Initial ThroughputAnd
Coarse Alignment
Control And
Optimization
Bonding
Post-BondTesting
Unloading
End
Manufacturing Process
Current State-of-the-Art
LIMITATIONS:
Multi-modal Functions
Multi-Axes convergence
Slow, expensive
“Hill-Climbing” TechniqueVisual Inspect
and Manual Alignment
Initialization Loop
Move to set point (Xo)Measure Power (Po)
Stop motion Fix Alignment
ApproximateSet Point=Xo
Assembly Alignment Task Parameters
Off the shelfMotion Control (PID)
(Servo Loop)
StopStop
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 (Xo)Measure Power (Po)
Stop motion Fix Alignment
Set Point=Xo
Learning AlgorithmModel Parameter
AdjustmentOptical Power
Propagation Model
Correction to Model Parameter
{Xk}, {Pk}
FEED - FORWARD
Off the shelfMotion Control (PID)
(Servo Loop)
Assembly Alignment Task Parameters
Model Based Control Theory
)ˆ()(
)( 1p
d
KPsP
sR
1)(
)(
PK
P
sR
sP
p
r
)(
)(
)(
)(
)(
)(
sR
sP
sP
sR
sP
sP r
dd
r
Kp
Kp
Pd(s) Pr(s)++ +
-
R(s) E(s) P
1ˆ P
1)1
)(ˆ()(
)( 1
PK
PKP
sP
sP
pp
d
rIf = P,P̂
2),(1),(2r
eU
j
zyxU
jkr
Optical Modeling TechniqueUse 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 Domain Fourier Domain Spatial Domain
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
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
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
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
Nominal Model
dt
du
-
KK
KK
+ )1(
1
s
Proportional Gain
Proportional Gain
Motor Dynamics Plant Model
Derivative
Desired Power
Time Taken = 7 seconds
Model Based Control System
(1.41)
+InverseModel
+
+
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 )
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
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