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

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