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DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

MALAVIYA NATIONAL INSTITUTE OF TECHNOLOGY JAIPUR

JANUARY 2014

A

Seminar Report

on

Photonic Crystal Fiber

SUBMITTED BY:SIDDHARTH SETHIA2010UEC104VIII SEM , B.TECH

CANDIDATE DECLARATION

I hereby declare that the seminar report entitled, “Photonic Crystal Fiber” has been prepared

by me under the guidance and supervision of Dr. R K Madilla during the Academic Session

2013-2014.

SIDDHARTH SETHIA

ID: 2010UEC104

Final yr B.Tech.

Electronics and Communication Engineering.

1

CERTIFICATE

This is to certify that Siddharth Sethia, Institute ID No. 2010UEC104, VIII Semester, B.Tech.

Electronics and Communication Engineering has successfully completed his seminar work

entitled “Photonic Crystal Fiber ”.

The seminar report is found to be satisfactory and approved for submissions.

Date: Dr. R K Madilla

Asst. Professor

Department of ECE Engg.

MNIT, Jaipur

2

ACKNOWLEDGEMENT

I take this opportunity to express my deep sense of gratitude and respect towards Dr. R K

Madilla, Assistant Professor, Dept. of Electronics and Communication Engineering,

Malaviya National Institute of Technology, Jaipur. I am very much indebted to him for the

generosity, expertise and guidance I have received from him while working on this seminar

and throughout my studies.

I would also like to thank seminar coordinators Dr. K K Sharma Sir ( Professor, Dept. of

Electronics and Communication Engineering, MNIT, Jaipur)and Dr. M S Ansari Sir

(Assistant Professor, Dept. of Electronics and Communication Engineering, MNIT

Jaipur), who helped me for the completion of my report.

Siddharth Sethia

2010UEC104

3

ABSTRACT

A novel approach towards load forecasting has been proposed which predicts the data 1h

into the future. The prediction has been done with a moving window manner which is based

on real time data collected from ISO New England. Load Forecasting is an important tool

required in the area generation control and resource dispatch. The data collected is usually

noisy and have complicated load features. The authors of the paper after getting motivation

from their previous work have presented a wavelet neural network with data pre-filtering.

The main idea is to incorporate in the system, is a technique to detect spikes in load data and

correct them. Wavelet decomposition is used to decompose the load profiles into multiple

components at different frequencies; separate neural networks (NN) are applied to capture the

individual features. The features are then combined to form the final forecasts. In order to

employ a moving forecast, 12 wavelet NN are them applied on test results. The wide range of

comparison thus validates the effect of data pre-filtering and the accuracy of wavelet NN

based on the data set collected from ISO New England.

4

CONTENTS

PAGE NO

CERTIFICATE1

ACKNOWLEDGEMENT 2

ABSTRACT 3

CONTENTS 4

LIST OF FIGURES 6

CHAPTER1. Introduction 7

2. Brief History of PCF 8

3. Physical Properties 10

3.1 Effective Refractive Index

3.2 Filling Factor

3.3 Attenuation

3.4 Dispersion

3.5 Non-Linearities

4. Fabrication 14

5. Applications 20

6. Future Research Directions 22

7. Conclusion 24

References 26

5

LIST OF FIGURES

FIGURE NO. TITLEPAGE NO

Fig.3.1. Scatter plot of the actual load vs. the model 14

Fig.3.2. Correlation between the actual load and the model 15 Fig.4.1. Amplitude Spectrum for 5 min load data 20 Fig.4.2. Three channel filter bank 21 Fig.5.1. Neuron Mathematical Model 24 Fig.6.1. Difference in prediction after wavelet decomposition 27

Fig.6.2. Box plots for forecasting errors for 5 to 60 min outs 29

6

CHAPTER - 1

INTRODUCTION

Photonic-crystal fiber (PCF) is a new class of optical fiber based on the properties

of photonic crystals. Because of its ability to confine light in hollow cores or with

confinement characteristics not possible in conventional optical fiber, PCF is now finding

applications in fiber-optic communications, fiber lasers, nonlinear devices, high-power

transmission, highly sensitive gas sensors, and other areas.

Photonic crystal fibers may be considered a subgroup of a more general class

of microstructured optical fibers , where light is guided by structural modifications, and not

only by refractive index differencesevaluations of various sophisticated financial products on

energy pricing offered by the market.

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CHAPTER - 2

BRIEF HISTORY OF PCF

The PCF domain started by the idea that light could be trapped inside a 2D photonic crystal

made of silica capillaries running along the fiber length. At that time, the idea was to drill

microscopic holes inside a silica rod, then to draw it down to fiber. However, this technique

could not be used since the instruments were not able to drill into silica. The first successful

PCF was made by stacking several silica capillaries, then drawing the whole structure down

to fiber. One important parameter describing the fiber geometry is the diameter-to-pitch ratio

d/pitch. For this first PCF prototype, the ratio of 0.2 proved to be too small for bandgap

guidance, therefore the central capillary was replaced by solid silica for standard index

guidance.

Meanwhile, the Maxwell equations were numerically solved and proved that a bandgap could

occur inside a photonic crystal fiber.

(Birks, Roberts, Russel, Atkin, and Shepherd, "Full 2-D photonic bandgaps in silica/air

structures”,1995 )

Three years later, the first PCF showing photonic bandap guidance was fabricated using the

stack-and-draw technique. It was only 3cm long, so propagation losses could not be

measured. This figure here shows the field pattern at the fiber exit. Here, the light confined in

the core is composed of two different wavelengths whereas the cladding modes support the

whole input spectrum. So that was the brief introduction to photonic crystal fibers. As I

mentioned, numerical calculations of the guided modes were performed in order to verify that

bandgap guiding was indeed possible in hollow-core fibres.

1970: idea that a cylindrical Bragg waveguide can guide light

1991: idea that light could be trapped inside a 2D PhC made of silica capillaries.

– How? By drilling microscopic holes inside a silica rod, then drawing to fiber.

1995: Theoretical proof that bandgap guiding is possible

1996: First PCF prototype

8

Fig 2.1 Fig2.2

1999: first HC PCF

– Photonic bandgap guidance

– Stack-and-draw technique

– 3 cm long!

– When illuminated with white light:

Fig 2.3

9

Fig 2.4 Fig 2.5

CHAPTER - 3

PHYSICAL PROPERTIES

There are hollow-core fibres, where guiding is due to bandgap effect. This is the last

generation of hollow-core fibres, made by Crystal Fibers in Danemark. One thing that

prevents photonic crystal fibers from being commonly used in telecommunications is the loss

figure, which is 2 orders of magnitude higher than in standard fibers. Also, that the filling

factor had to be relatively high for a photonic bandgap to appear, and here its value is only

10%.

Attenuation: 10 dB/km

SMF = 0.2 dB/km

lc=1535nm, ∆l=100nm

neff~0.99

f ~ 10%

Fair = 95%

Fig 3.1

10

dcore

=10.6µm

Fig 3.2

There are also solid-core fibers, where guiding occurs by total internal reflection. These

fibers are less subject to variations of the microstructure and are therefore more robust and

more cost-effective than hollow-core fibers. Usually, PCFs are multimode, but a careful

design of the photonic crystal can lead to single-mode propagation for all wavelengths. For

shorter wavelengths, the cladding index increases, therefore the V-parameter stays low and so

does the total number of modes.

Attenuation: 0.5 dB/km

neff~1.4

Fair=1-15%

Index guiding -> robustness

Fig 3.3

11

Fig 3.4 Fig 3.5

ATTENUATION MECHANISM:

A key parameter describing a fiber (and also a waveguide) is the attenuation per unit length.

Here are the attenuation mechanisms. The insertion loss is quite important and depends on

the mode field diameter of the two butt-coupled fibers. There is also the confinement loss,

which is related to the imaginary effective index and decreases very fast with each additional

layer of air holes. Finally, scattering at the glass-air interface accounts for the majority of the

linear loss in most fibers.

Confinement loss:

Propagation loss:

– Rayleigh scattering (dopants) l-4

– Roughness of the glass-air interface l-3

– Impurities (OH- water absorption peaks)

– Bending loss:

– Lower than in plain silica fibers

12

Fig 3.6

NON LINEARITIES:

The nonlinearity of a fiber can be tailored. The effective nonlinearity of a fiber is a function

of the nonlinear refractive index as well as the effective area. Since the minimum achievable

radius is a function of the numerical aperture, extremely small effective areas are achievable

in solid-core fibers, which give huge nonlinearities. In complete contrast, low levels of

nonlinearity in hollow-core fibers make them very attractive for high power delivery

applications.

In step-index fibers: gmax ~ 30 (W.km)-1

In solid-core PCF: gmax ~ 550 (W.km)-1

– amin is function of NA: low Aeff

In hollow-core fibers: gmin ~ 0.01 (W.km)-1-> very attractive for high power delivery

applications

13

CHAPTER - 4

FABRICATION

Now, maybe you would like to know how to fabricate these fantastic fibers. Well, the first

step is to obtain a preform, which is a macroscopic version of your photonic crystal fiber. It is

about 2cm wide and can be fabricated by various methods depending on your glass material.

The most used technique when working with silica is capillary stacking whereas drilling and

extrusion are mostly used with soft glasses & polymers.

14

Fig 4.1

CAPILLARY STACKING:

The first step in stacking capillaries is to reduce their dimensions from 1-3cm to ~1mm using

a fiber drawing tower. Then, these smaller tubes are horizontally stacked together in an

hexagonal configuration. A silica rod placed at the center will become the solid fiber core and

several missing capillaries will become the hollow core of the fiber.

Reduce the dimensions of silica capillaries from 1-3 cm to ~1mm.

-> Using a fiber optics drawing tower

Horizontally stack in an hexagonal configuration

15

Capillary stacking technique

Fig 4.2

Fig 4.3

Fig 4.4

EXTRUSION:

As for the extrusion technique, it is used to fabricate the majority of soft glass preforms

(tellurite, chalcogenide, lead silicate, bismuthate,…). In this technique, molten material is

forced through a die containing a designed pattern of holes. The die can be made of stainless

steel for example. The extrusion technique was used to fabricate the first nonlinear solid-core

PCF in 2002. It was made of SF6 Schott glass, which has a softening temperature of about

500°C. The extrusion technique cannot be used with silica since its softening point is high

enough to melt the die material.

Used to fabricate the majority of soft glass preforms

16

– Tellurite, chalcogenide, lead silicate, bismuthate, …

Molten material is forced through a die.

Used to fabricate the first nonlinear solid-core PCF in 2002 (lead silicate glass,

Tsoft=538°C, n=1.805)

Fig 4.5

Fig 4.6

PREFORM:

Once you obtain your preform, you draw it down to fiber. Usually two drawing runs are

required to achieve micrometric dimensions. In the first run, the preform is reduced to a cane

of 1mm diameter. Then this cane is introduced inside a jacketing tube and drawn to the final

fiber. One important thing to realize is that the drawing process is not a lithographic process,

where the final structure would be the structure of the preform scaled down. For example, by

changing the pressure inside the holes during drawing, the holes can be enlarged or shrinked.

Preform is heated to 2000°C to soften silica

17

Collapse ratios of ~50,000

1st run: cane of 1mm diameter

2nd run: cane is introduced inside a jacketing tube + drawn to the final fiber

Not a lithographic process!

Fig 4.7 Fig 4.8

FINAL PROCESS:

Once the final dimensions are reached, the fabricated fiber is coated with a polymer jacket to

improve its mechanical strength. This is done by applying a liquid polymer (acrylate) to the

optical fiber, then it is solidified by a UV lamp. The final product is then wound onto large

spools and is ready to be shipped to you.

18

Fig 4.9

Fig 4.10

CHAPTER - 5

APPLICATIONS OF PHOTONIC CRYSTAL FIBER

I will finish this talk by presenting two applications where a lot of research work in being

done. This is the first report of white-light supercontinuum generation in photonic crystal

fibers. A fs pulse was sent into the fiber at 800 nm. The dispersion was engineered to have

19

the zero dispersion wavelength at the laser wavelength. The small effective area in this all-

silica fiber futher enhanced the nonlinear effects. For more details on nonlinear optics in PCF,

there is this very nice article recently published in Nature Photonics.

First report of white-light supercontinuum generation:

– A 100 fs pulse was sent @ l=800nm. Peak power=kW

– Dispersion was engineered to have ZDW @ l=800nm.

Small Aeff to enhance nonlinear effects

Fig 5.1

Since PCF can be filled with gases or liquids, another interesting application is to make gas

cells and to study light-matter interactions. Here is a transmission spectrum of a hollow-core

fiber filled with acetylene gas. This fiber can then be used as a frequency reference.

All-fiber gas cells

20

Fig 5.2

Fig 5.3

21

Also, by filling fibers with liquids, temperature sensors can be fabricated. When a certain

wavelength is reached, the effective index of the guided mode reaches that of the liquid and

light becomes evanescent in the core. Since the refractive index of the liquid is a function of

temperature, temperature monitoring can be made.

Temperature sensors

Fig 5.4

Fig 5.5

22

CHAPTER - 6

FUTURE PROSPECTS

LATERAL THINKING WITH PHOTONIC CRYSTAL FIBERS:

The idea of this paper is illustrated schematically in Figure 1a, where a microstructured

optical fiber preform is first fabricated on a macroscopic scale and then drawn to the desired

dimension to create the TMC. It is then cleaved and the resulting microstructure is

interrogated transversely. Figure 1b and 1c illustrate the conceptual difference between a

planar device fabricated lithographically and fiber process mentioned above. As can be seen,

similar structures could be placed in a microstructured fiber as could be written into a planar

chip. However, the fiber draw process can produce many such fiber devices at once and the

resulting smoothness of the microstructure would give far superior optical performance over

lithographically written devices [4]. Also, due to the high longitudinal consistency in the fiber

draw process, the fiber devices have the same microstructure along their entire length. This

makes any structure in the fibers semi-infinite in extent, providing the “Tall” in TMC.

In conclusion, we introduce a novel geometry utilizing fibers transversely as an effective

alternative to planer photonic chip devices. We successfully demonstrate this concept using a

photonic crystal fiber that is dynamically switched using microfluidics.

23

MICROSTRUCTURED FIBER:

Fig 5.6

24

CHAPTER - 6

CONCLUSION

Accurate load forecasting is very important for electric utilities in acompetitive environment

created by the electric industry deregulation. The review of some statistical and artificial

intelligence techniques that are used for electric load forecasting has been done. The factors

that affect the accuracy of the forecasts such as weather data, time factors, and customer

classes, as well as economic and end use factors are discussed. Load forecasting methods use

advanced mathematical modeling. Additional progress in load forecasting and its use in

industrial applications can be achieved by providing short-term load forecasts in the form of

probability distributions rather than the forecasted numbers. This paper presents a method of

wavelet neural networks with data pre-filtering to forecast very short-term loads 1 h intothe

future in 5-min steps in a moving window manner. The spike filtering methods remove spikes

in real-time. This WNN method can capture the load components at different frequencies.

Daubechies-4 with two-level decomposition is the best configuration, which balances the

decomposed level, the filter length, and the minimum padding length for decomposition.

Symmetrization is shown to be the best strategy to handle the distortion. Applying the

relative increment transformation to load series enhances the load stationarity. Based on test

results, 12 dedicated wavelet neural networks are used to performmoving forecasts every 5

min. Numerical testing shows accurate predictions with small standard deviations for VSTL

based on the data set from ISO New England.

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