LVTS Dose&Focus recognition by image

Is it possible to make the life

of Litho Engineer easier or how

to trace DOSE and FOCUS by

image

Vladislav Kaplan, July 2008

Agenda• Classical FE’s

▫ Purpose

▫ Mathematical presentation

▫ Algorithms and models

• Way of Control – CD (CDSEM)

▫ Methodology of measurements

• Is it another way…?

▫ Methodology of Image Processing

▫ Dose/Focus correlated parameters

▫ Classification

▫ SW presentation

• Things to be concerned with…and next steps

▫ Litho tool impact

▫ Metrology tool impact

▫ Layer/Mask impact

Classical FE

FE

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

200.0

Focus

steps

Focus

CD

's

Dose0

Dose1

Dose2

Dose3

Dose4

Dose5

Dose6

Classical FE’s

• Choose most sensitive feature. (Could be more than one)

• Run FE.

• Define best dose and focus based on allowable process window.

• Control it with CDSEM.

Classical FEFE

140.0

145.0

150.0

155.0

160.0

165.0

170.0

175.0

180.0

185.0

190.0

Focus

steps

Focus

CD

's

Dose0

Dose1

Dose2

Dose3

Dose4

Dose5

Dose6

Wide process window.

• PW – 160-180 nm

• Dose2Focus5 optimal.

• Still in PW up to +/-2 steps for focus

and up to 1 step for dose

fluctuation.

Tight process window.

• PW – 170-180 nm

• Dose2Focus5 still optimal.

• Still in PW up to +/-1 steps for focus

and no allowable dose fluctuation.

CDSEM.

• For CD measure 175nm dose

fluctuation could from Dose0 to

Dose2 including and +-2 step of

focus, so CDSEM could not be a

”definitive tracer” of litho tool dose

and focus stability.

CDSEM measurement

Grayscale level approach for CD measurement

• Build radial vector.

• Apply LPF alongside the vector.

• Find cross over threshold location.

• Calculate distance from center to location.

• Repeat it N times with different angles.

• Find average.

CDSEM pros and cons.

• Excellent for predefined feature measurement.

• Good for edge location definition.

• Bad for form of feature characterization.

• 90 percent of pixel information in image lost.

Morphology

• Discipline in Image processing specialized on

extraction and characterization of binary images.

Could be very helpful for form description and

recognition.

Examples of Morphology measurement

• Threshold image – get binary one. (0 – black or 1 – red)

• Fill holes

• Reduce particle of particular area or shape – irrelevant for decision making

• Calculate morphology parameters for each remaining particle.

• Parameters could be:

• Elongation, Orientation, Type factor, Waddler disk diameter, Heywood

circularity factor, Compactness factor, Different moments of inertia etc..

ConclusionIn order to increase amount of information used for processing

need to add morphology operations and particle analysis.

FE after morphology operation

1. By replacing CD measurement by particle morphology area

measurement be getting better separation per dose.

FE

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

200.0

Focus

steps

Focus

CD

's

Dose0

Dose1

Dose2

Dose3

Dose4

Dose5

Dose6

FE per Via area

0

1000

2000

3000

4000

5000

1 2 3 4 5 6 7 8

Focus

Are

a

D0

D1

D2

D3

D4

D5


FE

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

200.0

Focus

steps

Focus

CD

's

Dose0

Dose1

Dose2

Dose3

Dose4

Dose5

Dose6

1. By replacing CD measurement by particle orientation

measurement we getting strong separation between negative and

positive focus.

FE per Via Orientation

0

50

100

150

200

1 2 3 4 5 6 7 8

Focus

De

gre

es

of r

ota

tio

n

D0

D1

D2

D3

D4

D5


FE

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

200.0

Focus

steps

Focus

CD

's

Dose0

Dose1

Dose2

Dose3

Dose4

Dose5

Dose6

1. By replacing CD measurement by particle Area*sign[orientation]

measurement we getting strong separation between negative and

positive focus as well as better dose separation..

FE per Via Area combined Orientation

-6000

-4000

-2000

0

2000

4000

6000

1 2 3 4 5 6 7 8

Focus

Area*sig

n[O

rie

ntatio

n]

D0

D1

D2

D3

D4

D5


FE

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

200.0

Focus

steps

Focus

CD

's

Dose0

Dose1

Dose2

Dose3

Dose4

Dose5

Dose6

1. By replacing CD measurement by particle Elongation

measurement we getting visible separation of dose.

FE by Elongation

1

1.1

1.2

1.3

1.4

1.5

1 2 3 4 5 6

Dose

Elo

ng

ati

on

Focus0

Focus1

Focus2

Focus3

Focus4

Focus5

Focus6

Focus7

What we are trying to characterize

1. Via changing orientation from <90 degree to > than 90 degree as result of

focus changes

2. Via changes its area as result of focus change

3. Via changes its elongation as result of focus change

4. For every other dose this process has different rate.

Engine for classification

1. Two obvious ways for recognition of Focus/Dose imaging sets are fuzzy

classifier and Minimal Mean Distance classifier.

2. Advantages for Fuzzy classifier –

• Strait forward – just define the required range of values per Dose/Focus

imaging set.

• Simple – no need for complicated mathematical operation, for example

normalizations and decompositions.

3. Disadvantages for Fuzzy classifier –

• Build as is –SW need to be recompiled in order to correct/change any

additional parameter or information.

• Highly Non-linear, rigid structure – problematic tool for research and

definition for new parameters

We choose MMDC – for the research purposes and relative simplicity in

reconfiguration.

Simple MMDC classifier

SW demonstration - Cool

Results

Focus0Dose0 Focus2Dose0 Focus1Dose5 Focus5Dose3 Focus5Dose0 Focus4Dose0


















Sign [Orientation]*Area*Elongation

Sign [Orientation]*Area; Area; Area*Elongation

Sign [Orientation]*Area*Elongation; Area

For all cases high

probability to find

Dose/Focus value

within one step of

dose or/and focus

error

Next stepsNeed significant amount of FE data in order to:

• Characterize stability of measurement factors.

• Create real MMDC – not just on one set of FE’s

• Create robust normalization of parameters in SW – Fisher decomposition?

Need significant amount of real CDSEM images from production recipes:

• Full verification process

Need CDSEM recipe optimization – close 0 nm shifts in array

• Characterize stability of measurement factors.

• Full verification process

ConclusionSimilar process of focus/dose characterization possible to perform on any closed

shape feature, not just via. In that case amount of information that we could

extract with the help of morphology operations are greater significantly.

Engineering

LVTS Dose&Focus recognition by image