critical dimension control -...

10/6/2003 FLCC Research Seminar 1

Critical Dimension Control

FLCC Research Seminar 10/06/03Costas J. Spanos


Outline• What is CD and why it matters• Anatomy of CDU • Present Control Status• The Post Exposure Bake factor• Plasma Etch CDU Opportunities• A combined litho/plasma controller framework?• What is next


What is the Critical Dimension?CD

Gate is typically made out of Polysilicon


Basic CD Economics

(D. Gerold et al, Sematech AEC/APC, Sept 97, Lake Tahoe, NV)

Today’s data suggests a “price” of more than $10/nm…Today’s data suggests a “price” of more than $10/nm…


Why was this Improvement Important?

600k APC investment, recovered in two days...


The 2002 Roadmap


Projected Parametric Variation in Future Nodes

(assuming fixed overall % in CD variation)Delay and Energy variation caused by lint variation -

8 Bit Mirror Adder

0

10

20

30

40

50

60

70

80

0 50 100 150 200Technology Node (nm)

% V

aria

tion

caus

ed b

y lin

t

Linear (% Delay variation caused by lint variation )Linear (% Energy variation caused by lint variation )


Outline• What is CD and why it matters• Anatomy of CDU• Present Control Status• The Post Exposure Bake factor• Plasma Etch CDU Opportunities• A combined litho/plasma controller framework?• What is next


Statistical Metrology for CDs

Spatial

Random/Deterministic

Within Die

Among Die

Among Wafers

Causal

Equipment Recipe

?Other

Response from Equipment A

Random/Deterministic


Nested Variance – A Simple Model

CDijkl = µ + fi + wj + lk + Ll + σTrue mean

Across-field

Across-wafer

Across-lot

Lot-to-lot

Random

CD variation can be thought of as nested systematic variations about a true mean:

Field

Wafer

Lot

Jason Cain, SPIE 2003


Across-Wafer Systematic Variation

- -

=

Average Wafer Scaled Mask Errors Across-Field Systematic Variation

Across-Wafer Systematic Variation

Separation of Across-Field and Across-Wafer Systematic Variation:σ2 = (3.28 nm)2 σ2 = (1.77 nm)2 σ2 = (1.23 nm)2

σ2 = (2.46 nm)2


Across-Wafer Systematic VariationThe remaining across-wafer systematic variation was removed using a polynomial model:

CD(Xw,Yw) = aXw2 + bYw

2 + cXw + dYw + eXwYw

- =

Across-Wafer Systematic Variation with bivariate

Gaussian removed

Polynomial Model Remaining Across-Wafer Systematic Variation

σ2 = (2.39 nm)2 σ2 = (1.39 nm)2 σ2 = (1.95 nm)2


Systematic Variation Model Summary• Across-field systematic variation:

– Mask errors (magnified scaling factor) removed– Polynomial terms in X and Y across the field

• Across-wafer systematic variation:– Across-field systematic variation removed– Bivariate Gaussian for spot effect (likely linked to develop effect)– Polynomial terms in X and Y across the wafer

Field WaferMetrology?Process replication?


CD Variability Summary• Lot to Lot: controlled by “standard” EWMA

controllers. – Accounts for less than ~1/3 of CDU.

• Within Lot: not actively controlled.– Rapid equipment drift. Exposure to PEB timing. PEB

Bake Oven match. Etch chamber match.• Across Wafer: not actively controlled.

– Stepper stage, PEB plate uniformity, Develop uniformity, etch chamber uniformity.

• Across Field: not actively controlled.– Mask manufacture, optics, mask error factor, feature-

level compensation (OPC).


The Workcell ControllerMost process steps are so interrelated that must be controlled together using feed forward and feedback loops.Crucial pieces of equipment must by controlled by SPC throughoutthis operation.

EquipmentModel

Step

EquipmentModel

Step

AdaptiveControl

AdaptiveControl

test test test


The Principle behind profile inversion


Switching Spaces – Going directly to Process Settings


Results for 27k-entry Library with external PEB sensors

Foc. FittingR2 = 0.9955

-0.5

-0.3

-0.1

0.1

0.3

0.5

-0.5 -0.3 -0.1 0.1 0.3 0.5

Actual Foc.

Estim

ated

Foc

.

Foc. FittingR2 = 0.9955

-0.5

-0.3

-0.1

0.1

0.3

0.5

-0.5 -0.3 -0.1 0.1 0.3 0.5

Actual Foc.

Estim

ated

Foc

.

PEB Tim e Fitting R2 = 0.9733

57585960616263

57 58 59 60 61 62 63

Actual Time

Estim

ated

Tim

e

PEB Tim e Fitting R2 = 0.9733

57585960616263

57 58 59 60 61 62 63

Actual Time

Estim

ated

Tim

e

PEB Tem p Fitting R2 = 0.9868

128

129

130

131

132

128 129 130 131 132

Actual Tem p

Estim

ated

Tem

p

PEB Tem p Fitting R2 = 0.9868

128

129

130

131

132

128 129 130 131 132

Actual Tem p

Estim

ated

Tem

p

Exposure FittingR2 = 0.9849

28.0

28.5

29.0

29.5

30.0

28.0 28.5 29.0 29.5 30.0

Actual Exp.

Estim

ated

Exp

.

Exposure FittingR2 = 0.9849

28.0

28.5

29.0

29.5

30.0

28.0 28.5 29.0 29.5 30.0

Actual Exp.

Estim

ated

Exp

.


A Profile Inversion Scheme?

Spin Coat, PAB

PEB, Develop SSSExposure

Profile Inversion Engine


PEB Evolved from a Single Zone to Multi-Zone Control System Why?

Multi-Zone ControlSingle Zone Control

8 Years of Product

Evolution

PEB Track Equipment EvolutionComplexity is Increasing


Monitoring Plate Drift

Spread 0.272 °C One week later Spread 0.384 °C

Same wafer, same plate

No appreciable drift in mean


Auto-Calibration ResultsSteady State Range Optimization

Settings in Use After OnWafer Auto-Calibration

0.506 °C 0.277 °CTwo

OnWafer “missions”


Across plate CD map improvement Example

95 96 97 98 99 100

95 96 97 98 99 100

3-21 10nmC

-150

-100

-50

0

50

100

150

y

-150 -100 -50 0 50 100 150

x

Contour Plot

-150

-100

-50

0

50

100

150

y

-150 -100 -50 0 50 100 150

x

Contour Plot

Target

before

after

CD Map

CD Map


90 91 92 93 94 95 96 97 98 99 100

90 91 92 93 94 95 96 97 98 99 100

10nm Corr

Aggregate Improvement on several plates(achieved ~60% sigma reduction)

Target

before

after


But how about the transient PEB impact?

P6P6P5P5P4P4P3P3P2P2P1P1

Six plates used for 90nm CD lines (193nm Lithography)Six plates used for 90nm CD lines (193nm Lithography)


CD Spread vs “Overshoot” Spreadcorrected for “Cooling Rate Spread”.

2

4

6

8

10

12

Thre

e-S

igm

a Le

vera

ge R

esid

uals

1.50 1.75 2.00 2.25 2.50Overshoot Ranges Leverage, P<.0001

45

6

1

3

2


Spatial CD Dist. Correlation


Auto-Calibration ResultsTransient Range Optimization

1.859 °C 0.715 °C

Settings in Use After OnWafer Auto-Calibration

Two OnWafer

“missions”


The CDU opportunities in plasma…

107 105

105105

Note how the three runs on 105 have the same sloppy shape, while the one run on 107 is flatter…


CDU opportunities in plasma

“before” data is hotter, further, the pre-etch step is significantly less uniform…“before” data is hotter, further, the pre-etch step is significantly less uniform…


CDU Opportunities in Plasma:balancing the He zones

beforebefore afterafter


CDU Opportunities in Plasma Chamber A vs Chamber C

Main Etch A Main Etch C

Post Etch CPost Etch A


Outline

• What is CD and why it matters• Anatomy of CDU • Present Control Status• The Post Exposure Bake factor• Plasma Etch CDU Opportunities• A combined litho/plasma controller framework?• What is next


Present Status of “Active” CD Control

Spin

HMDS

PA Bake

Exposure

PEB

Develop

PD Bake

Photoresist Removal

ELMELM

Poly Etch System

ADIADI AEIAEI

Etch

Etch

Etch


On-wafer and in-situ metrology in pattern transfer

Spin

HMDS

PA Bake

Exposure

PEB

Develop

PD Bake

Photoresist Removal

ELMELM

Poly Etch SystemEtch

Etch

Etch

T (t, x, y)T (t, x, y)

T (t, x, y)V (t, x, y)E (t, x, y)…

T (t, x, y)V (t, x, y)E (t, x, y)…

I (x, y)I (x, y)

OCDOCD

Thin FilmThin Film

OCDOCD OCDOCD


New style CDU control has to incorporate many strategies

Spin

HMDS

PA Bake

Exposure

PEB

Develop

PD Bake

Photoresist Removal

ELMELM

Poly Etch SystemEtch

Etch

Etch

T (t, x, y)FF control

T (t, x, y)FF control

T (t, x, y)V (t, x, y)E (t, x, y)FF/FB Control, chuck diagnostics

T (t, x, y)V (t, x, y)E (t, x, y)FF/FB Control, chuck diagnostics

I (x, y)Optimal Pattern Design

I (x, y)Optimal Pattern Design

OCDProfile Inversion

FB Control

OCDProfile Inversion

FB Control

Thin FilmFB/FF Control

Thin FilmFB/FF Control

OCDFB Control

OCDFB Control

OCDFB/FF Control

OCDFB/FF Control


What is the ultimate CDU?

• With OCD, and PEB FF control, across wafer 3σ of 2nm (300mm wafers, 90nm lines, 193nm litho) has been demonstrated.

• Through transient control and OCD, we expect to remove another 0.5nm from the 3σ value.

• State of the art 3σ of 1.0~1.5nm CDU is possible if plasma chamber matching is included in the mix.

• All of these techniques are scalable, and will be amortized (andneeded!) for EUV.


What remains to be seen• Will the addition of more autonomous sensors help?

– On-wafer aerial image sensor– On-wafer multi factor plasma sensor

• What will be the impact on mask fabrication uniformity?• Will “profile” inversion needs finally bring first principle

modeling into routine manufacturing operation?• Will these ideas be amortized beyond pattern transfer?

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