8:30 – 9:00 research and educational objectives / spanos

8:30 – 9:00 Research and Educational Objectives / Spanos

9:00 – 9:50 Plasma, Diffusion / Graves, Lieberman, Cheung, Haller

9:50 – 10:10 break10:10 – 11:00 Lithography / 10:10 – 11:00 Lithography / Spanos, Neureuther, BokorSpanos, Neureuther, Bokor 11:00 – 11:50 Sensors & Metrology / Aydil, Poolla, Smith, Dunn

12:00 – 1:00 lunch 1:00 – 1:50 CMP / Dornfeld, Talbot, Spanos

1:50 – 2:40 Integration and Control / Poolla, Spanos

2:40 – 4:30 Poster Session and Discussion, 411, 611, 651 Soda 3:30 – 4:30 Steering Committee Meeting in room 373 Soda 4:30 – 5:30 Feedback Session

3rd Annual SFR Workshop, November 8, 2000

11/8/00

2

Lithography

SFR Workshop

November 8, 2000

Costas Spanos, Jeffrey Bokor,

Andy Neureuther

Berkeley, CA

11/8/00

3

Outline

• Sensitivity of Scatterometry• Statistical Simulation and Optimization• Effects of line edge roughness• Novel lithography enhancements• Modeling and simulation

11/8/00

4

Sensitivity of Spectroscopic Scatterometry: Sub-100nm Technology

SFR WorkshopNovember 8, 2000

Ralph Foong, Costas SpanosBerkeley, CA

11/8/00

5

Why worry about the “limits” of Scatterometry?

• Capabilities of scatterometry and required equipment specifications need to be formalized for 100nm metrology.– Commercial ellipsometers have been identified as being able to perform

spectroscopic scatterometry. Hence, the focus of this study is on these equipment.

– Precision of current generation commercial ellipsometers in measuring profiles consistent with 100nm technology node has to be confirmed.

• Scalability of scatterometry towards 70nm and 50nm metrology has to be explored.– Minimum commercial ellipsometer specifications necessary to

successfully implement 70nm and 50nm metrology need to be determined.

11/8/00

6

Overall Framework of Sensitivity Analysis

Commercial Equipment Analysis

Profile Parameters

Simulations for variation in parameter X

[X(-),X(Nominal), X(+)]Cos

Lambda

Tan

Lambda

Determine Noise Contributions

Tan , Cos Noise Spectrum

Are Variations Detectable?

NoYes

EM Response Variations

Which part of the spectrum contains the

most information?

11/8/00

7

Methodology• Electromagnetic simulations

are conducted for small changes in profile parameters to measure variations in EM response.

• Noise analysis of commercial ellipsometers is carried out to determine detectability of EM response variations.

d(Beam Divergence)

d(ISource)

d(Polarizer)

d(Analyzer)

d(IDetector)

Sample

PR

ARC

Poly-Si

RoundingSlopeAngle

Height CDFooting

Si

11/8/00

8

Signal-to-Noise Ratio for SOPRA EllipsometerSignal-to-Noise Ratio vs Lambda

1

10

100

1000

10000

100000

1000000

10000000

100000000

0.19

0.23

0.27

0.31

0.35

0.39

0.43

0.47

0.51

0.55

0.59

0.63

0.67

0.71

0.75

lambda (nm)

Cts

/s (

Sig

nal

, N

ois

e),

SN

R

Intensity (cts/s)

Noise (cts/s)

S.N.R

• Signal averaged over 30 measurements

• Noise represents 1 standard deviation for each wavelength

• Empirical formula for signal-to-noise ratio:

Noise = 0.412(Intensity)0.632

(R2 Value = 0.937)

• Intensity fluctuation is the main contributor of measurement noise in ellipsometers.

• Monte-Carlo simulations incorporating intensity fluctuations are used to determine the final distributions of Tan and Cos

• The ‘Minimum Detectable Variation’ lines represent the sum of the 3 errors of each of the 2 profiles measured to obtain the variation.

• The graphs demonstrate a trend toward significant information contained in a narrow band in the lower wavelength spectrum.

11/8/00

9

100nm Technology Simulations

Variation of Tan Psi for CD Variation vs Lambda

0.0001

0.001

0.01

0.1

1

10

100

Lambda(nm)

Tan

Psi

Var

iati

on Variation(-)

Variation(+)

Minimum DetectableVariation

Undetectable (Below yellow

line)

Detectable (Above yellow

line)

Variation of Cos Del for CD Variation vs Lambda

0.00001

0.0001

0.001

0.01

0.1

1

Lambda(nm)

Co

s D

el V

aria

tio

n Variation(-)

Variation(+)



0.001

0.01

0.1

1

10

Lambda(nm)

Tan

Psi

Var

iati

on Variation(-)

Variation(+)



0.0001

0.001

0.01

0.1

1

Lambda(nm)

Co

s D

el V

aria

tio

n Variation(-)

Variation(+)



line)


line)

100nm Dense Lines (ASIC) 65nm Isolated Lines (MPU)

spectrum of information

content

11/8/00

10

70nm Technology Simulations70nm Dense Lines (ASIC) 45nm Isolated Lines (MPU)


0.0001

0.001

0.01

0.1

1

10

Lambda(nm)

Tan

Psi

Var

iati

on Variation(-)

Variation(+)



0.00001

0.0001

0.001

0.01

0.1

1

Lambda(nm)

Co

s D

el V

aria

tio

n Variation(-)

Variation(+)



0.0001

0.001

0.01

0.1

1

Lambda(nm)

Tan

Psi

Var

iati

on Variation(-)

Variation(+)



0.00001

0.0001

0.001

0.01

0.1

1

Lambda(nm)

Co

s D

el V

aria

tio

n Variation(-)

Variation(+)



line)


line)

11/8/00

11


0.00001

0.0001

0.001

0.01

0.1

1

Lambda(nm)

Tan

Psi

Var

iati

on Variation(-)

Variation(+)


50nm Technology Simulations50nm Dense Lines (ASIC) 30nm Isolated Lines (MPU)


line)


line)


0.001

0.01

0.1

1

10

Lambda(nm)

Tan

Psi

Var

iati

on Variation(-)

Variation(+)



0.000001

0.00001

0.0001

0.001

0.01

0.1

1

Lambda(nm)

Co

s D

el V

aria

tio

n Variation(-)

Variation(+)



0.000001

0.00001

0.0001

0.001

0.01

0.1

1

Lambda(nm)

Co

s D

el V

aria

tio

n Variation(-)

Variation(+)


11/8/00

12

Scatterometry Milestones• Characterize the capabilities of scatterometry in fulfilling the metrology

needs of the 100nm technology node, by 9/31/2001• Study the feasibility of 100nm profile extraction using small footprint,

in-line spectroscopic ellipsometry, by 9/31/2002• Implement lithography controller that merges full profile in-line

information with available metrology, by 9/31/2003

Profile Diagnostics

DUV Photolithograph

yPR Deposition,

Focus, Exposure, Bake Time,

Development Time, etc

Process Flow

In-Line Scatterometr

y

Process Flow

Wafers

Feedback Control Loop

11/8/00

13

Process Monitoring and Optimization Using Simulation and Full-profile

Metrology


Junwei Bao, Costas SpanosBerkeley, CA

11/8/00

14

Process Monitoring and Optimization Using Simulation and Full-profile Metrology

• Lithography simulators are mainly used to study the qualitative effects and trends of certain parameters.

• Importance of quantitative predictive capabilities is increasing with increasing development costs and time-to-market pressures.

• Extremely small process window leads to unstable process.

• More frequent and in-line measurement is needed to monitor process drift.

• Process recipe needs to be optimized to maximize the yield considering the effect of parameter variations.

11/8/00

15

• Design of Experiment– 24 wafers, patterned using focus-exposure matrix

– Different softbake temperature, PEB temperature and time, develop time

– Resist index variation due to bake temperature is very small – can be ignored for scatterometry library generation

Experimental Simulator Tuning and Process Characterization

Refractive Index on Wafer 1

0

0.5

1

1.5

2

2.5

0 2000 4000 6000 8000 10000

Wavelength (A)

n

k

Bake Effect on Resist Thickness and Index

5850

5900

5950

6000

6050

6100

2 3 4 11 14 16 18 21 22 23 24

Wafer #

Thic

knes

s (A

)

1.546

1.548

1.550

1.552

1.554

Index@673nm

Th

n

11/8/00

16

• Full-profile information is needed for simulator calibration due to high-dimensionality and non-linearity of the parameter space.

• Scatterometry– Non-destructive– High throughput and in-line capability– Full-profile information

• Other full-profile metrologies– Applied Materials, VeraSEM™3D Metrology SEM™– AFM– Cross-section SEM

Simulator Calibration using Full-profile Metrology

11/8/00

17

Short term Long term repeatabilityreproducibility repeatability

Bottom CD < 0.5 nm < 0.5 nm 0.8 nm

Trench depth < 1.5 nm < 1.5 nm 1.7 nm

Reproducibility: 10 consecutive measurements at same location with one time wafer loading

Repeatability: Each measurements was performed on fresh loading on the same wafer

Long-term data collected from June 9 to June 27

Scatterometry Repeatability Characterization Using STI Structure

11/8/00

18

Process Monitoring using in-line Metrology

ExposureSpin coat

& softbakePEB Develop

Process Monitor based on tuned simulator

ellipsometer

n & kthickness

reflectometer

resistthickness

reflectometer

DITL

scatterometer

resistprofile

11/8/00

19

Yield Prediction• Distribution of equipment settings

– Historical data obtained from equipment characterization

– Estimated from simulator-process mismatch estimation

• Process window calculation– Generate input parameter – output profile relation using

calibrated simulator

– Calculate process window according to profile constraint

• Predicting Yield of lithography process by overlap integration

11/8/00

20

Process Window Engineering• Change the shape of the process window by adjusting the

operating point settings and material parameters (if possible), so that the overlap integration between the joint p.d.f of inputs and the process window, i.e., the projected lithography yield, is maximized.

11/8/00

21

Litho Control Milestones

+-

RECIPEOPTIMIZER

Calibrated Lithography

Simulator

Simulated Output

distributions

Ope

rati

ng P

oint

dist

ribu

tion

s

Profileswithin spec.

Parameterdistributions

In-die spatialvariation

Overlapping to get yield

Demonstrate simulator tuning for full profile matching over a range of focus and exposure conditions by 9/30/2001.

Demonstrate lithography simulator tuning for full statistical profile matching over a range of conditions by 9/30/2002.

Implement lithography controller that merges full profile in-line information with available metrology by 9/30/2003.

11/8/00

22

Effects of LER on MOSFET Electrical Behavior


Shiying Xiong, Jeffrey BokorBerkeley, CA

11/8/00

23

Motivation

• Channel length variation

• Edge Electric field

• Enhanced dopant diffusion

• Problems with LER

• Possible effects on devices

• Change of device parameters from target values

( leakage, driving current, swing and etc.)

• Enhanced hot carrier degradation

• Isolation problem

11/8/00

24

LER device simulation• Base device design

• Gate Length = 0.1um• Buried Oxide = 100 nm• Si Film Thickness = 250Å• Gate Oxide = 30 Å

0.1um SOI NMOSFET with self-aligned source and drain

• Channel doping and halo selected to make Vt ~ 0.4V • Swing 70-80 mV/decade• Vdmax = 1.1V

• LER model• Digitized red noise

• LER parameters:

• RMS• Correlation Length ( ~1/fcutoff )

11/8/00

25

Simulation results• Device with diffusion (Source side is rough)

0 1 2 3 4 5 6 70.99

1.00

1.01

1.02

1.03

1.04

1.05

1.06

1.07

Lc=5nm

RMS (nm)

I on (r

elat

ive)

0 1 2 3 4 5 6 70.8

1.2

1.6

2.0

2.4

2.8

Lc=5nm

I off (

rela

tive)

RMS (nm)

• After diffusion:

–Junction is smoothed

–Leff is reduced

0 5 10 15 20 25 3040

45

50

55

60

65

70

75

80

85

Gate Edge Junction Leff

Y (n

m)

X (nm)

11/8/00

26

Simulation Results• LER enhanced dopant diffusion

0 1 2 3 4 5 6

7

8

9

10

11

12

13

14

Lc=5nm

So

urc

e D

iffu

sio

n L

eng

th (

nm

)

RMS (nm)

•Junction extracted from device simulation

• Further proof: 2D diffusion of doping boundary with LER

yxyxCy

tyxC

x

tyxCD

t

tyxC

,0,,

,,,,,,2

2

2

2

-20 -10 0 10 20 300

50

100

150

200

0 1 2 3 4 5 67

8

9

10

11

12

13

14

15

SQRT(4Dt)=5nm

Junc

tion

Diffu

sion

leng

th (n

m)

RMS (nm)

Lc=2nm Lc=10nm

0 20 40 60 80 1007

8

9

10

11

12

13

14

15

Correlation Length (nm)Ju

nctio

n Di

ffusi

on L

engt

h (n

m)

RMS=5.8nmSQRT(4Dt)=5nm

Significant enhanced diffusion when Lc smaller or comparable with diffusion SQRT(Dt)

11/8/00

27

Do we really need 3D simulations?• 2D simulation of abrupt junction devices of different channel length

lL

eqkT

Vdf

off eCLI

/)(

1)()( 0lL

offoff eLILISmall L

LCCLIon /)( 21

0.0116 0.0120 0.0124 0.0128 0.01320.570

0.575

0.580

0.585

0.590

0.595

0.600

0.605

0.610

0.615

2D Simulation Fit of Data

I on (

mA

/um

)1/Leff (1/nm)

76 78 80 82 84 86

1.0

1.2

1.4

1.6

1.8

2.0

2.2 2D simulation Exp. Fit of Data

Ln

(Io

ff)

(Ln

(nA

/um

))

Leff (nm)

11/8/00

28

Do we really need 3D simulations?

)/()( 23 umAIwAI DD• Try

0 1 2 3 4 5 6 70.8

1.2

1.6

2.0

2.4

2.8 3D simulation results 2D integral (eqn.(3))

I off (

rela

tiv

e)

RMS (nm)0 1 2 3 4 5 6 7

0.99

1.00

1.01

1.02

1.03

1.04

1.05

1.06

1.07

3D simulation results 2D integral (eqn.(3))

RMS (nm)

I on (

rela

tive

)

11/8/00

29

Starting experiments

SEM current scan over line

• Cooperation Project with AMD

• step one: LER extraction

150 160 170 180 190 200 2100

20

40

60

80

100

120

140

160

180

200

Y(n

m)

X(nm)

Rebuilt of the top line edge of ~50nm bottom width line

Average top width: 39.3nm, RMS1=2.5nm, RMS2=2.7nm, RMSW=2.7nm

11/8/00

30

Summary• Fast 2D simulation approach for LER effect was demonstrated

• LER effect can be captured by 2D simulations– Range of validity for roughness correlation length under study

• Collaboration on experiment with AMD finally underway

– LER measurement protocols being developed

Proposal Milestones

• Complete simulations for AMD structures (bulk device with channel length 70nm or less) - FALL 2000

• AMD wafers complete to gate level (TBD)• Characterization of gate roughness (TBD)• AMD wafers out (TBD)• Device characterization (TBD)

11/8/00

31

Novel Lithography Enhancements and Modeling/Simulation

SFR Review

November 8th, 2000

Kostas Adam, Mosong Cheng, Andy Neureuther,

Berkeley, CA

11/8/00

32

Threads, Objectives and Proposed Research

• Novel Optical Lithography EnhancementsScreen, quantify and reduce to practice techniques for

enhancing feature resolution and reproducibility

– Electric-field enhanced post-exposure bake

– Defect-probe based aberration monitors

– Polarization nanoimprinted masks

• Simulation of Lithography PerformanceQuantify and provide models for the impact on lithographic

performance of non-idealities in masks, optics and resists

– 3D phase defects on phase-shifting masks

– Roles of chemically-amplified resist and topography

– Calibration of electromagnetic simulation tools

ASML 248nm Experimental

Test Bed

11/8/00

33

3D Phase Defect Printability: Approach

Illumination: Linearly polarized plane wave (TE/TM), =248nm

Imaging: Demagnification=4X, =0.3, NA=0.68

The CDaerial is evaluated at the 30% level of the normalized intensity

illumination plane

observation plane (near field)

glass voidglass post

feature (Cr)

180o phase shift

vacuum (air)

quartz

CDmask

underetch

sized

defect height

Simulation with TEMPEST

11/8/00

34

3D Phase Defect Printability: Results – Near Field160 x 160nm (4X) post

160 x 160nm (4X) void

|E| Phase of E

|E| Phase of E

Glass posts and voids do not have the expected intensity and phase and this loosens and tightens defect tolerances.

11/8/00

35

New Model for Mask Edges: Approach

Goal: Model the bias between ideal and rigorous simulations to allowuse of the thin-mask approximation in design of OPC etc.

New Model:Modify mask geometry that is input in an ideal SPLAT simulation. This can bedone by extracting information from the near-field diffraction pattern of 2Drigorous simulations (TEMPEST).

a

b

a

b

a

b

a+a

b +b

So far: New Model:

Mask MaskIn SPLAT In SPLAT

Air

Quartz

Stack

Illumination: E

New edge location for ideal simulation

geometrical position of edge

50nm

11/8/00

36

Inspection of Phase Defects

Isolated quartz defect: Spatial variations of EM field (modes)

The defect proximity to the feature affects both its inspectability and its printability

0o 180o

Approach (similar to Socha’s contact hole inspection): - Excite a spatial mode within the defect and find the EM field outside- Time reversal: Use the calculated EM field as new illumination

excite defect mode

resulting EM field

11/8/00

37

Electric-Field-Enhanced Post Exposure Bake• A vertical electric field enhances the vertical drift/oscillation of the photoacid and

thereby improves the profile verticality and reduces the lateral acid diffusion.

• Al plates were coated with a film to prevent electrochemical reaction

• E-field: AC component and an upward DC bias to reduce T-topping.

Hotplate

Al platewafer

Al plate

Resist Ephotoacid

UVIIHS. 0.3, 0.2, 0.1m L/S, 12C/cm2. PEB 140oC, 90s. Dev. 60s. EFE-PEB: AC 9.8V, DC 0.65V, 3Hz.

Standard EFE-PEB

Iso Lines Not Open

Arrays Undercut and Gone Arrays Present

Iso Line Open

11/8/00

38

Rapid Imaging Algorithm for Resist RIAR

• 2-D reaction/diffusion time-evolving scheme

• Approximate species by parabolic polynomials during a time-step.

• Iteratively solve for the polynomial coefficients and reduce the time step until the error reaches certain criteria.

Comparison of STORM and RIAR complextity

0.1

1

10

100

1000

10 100 1000 10000

Number of nodes

CP

U t

ime

(se

c)

RIAR

STORM

CPU time of

STORM=O(N2)

CPU time of RIAR=O(N1.38)

For 625 nodes, STORM 3min, RIAR 20sec.

SPIE 00 Cheng

11/8/00

39

Line-End Shortening Modeling: Approach

MASK DATA

OPTICAL SIM

RESIST SIM

CD and LES-

EXP. DATA(193 nm Tool SEM)

2D MASK DATAMeasured on Mask

Analyze Discrepancy

TUNE RESIST MODEL

DESIGN SPECS

MASK, RESIST, LENS.

Line-end Shortening in 193nm Lithography: modeling and Simulation Mosong Cheng, Keeho Kim*, Mark Terry*, Maureen Hanratty*, Andrew Neureuther

*KFAB PEMT PATTERN GROUP Texas Instruments

RESIST PARAM

Sematech

TOOL DATA

(Aberrations)

11/8/00

40

Line-End Shortening Modeling: Results

193nm,0.75, resist thickness=350nm, BARC thickness=82nm. Real mask CD data were used.

140nm ISO-DEN bias

120

130

140

150

160

170

180

190

200

0 500 1000 1500 2000 2500 3000 3500

Pitch(nm)

CD(nm)

J(Exp)

J(Prolith)

170nm ISO-DEN bias

140

160

180

200

220

240

260

280

0 500 1000 1500 2000 2500 3000 3500

Pitch(nm)

CD(nm)

P(exp)

P(Prolith)

Simulation Compared to Exp. #2

11/8/00

41

Comparison of the calibrated resist model to LES at defocus 0.2m

SEM picture PROLITH simulation

11/8/00

42

Defect-Probe Based Aberration Targets: Approach

Grid is 0.1/NA

Dark Field Patterns

Probe

Coma

Trefoil

Spherical

Astigmatism

1 at 0o 1 at 180o

1 at 90o

11/8/00

43

Defect-Probe Based Aberration Targets: Results

0.068

No Aberration- Trefoil + Trefoil

0.366 0.190

Trefoil Sensitivity

11/8/00

44

Polarization Nanoimprint Mask: Approach

Reduced Proximity Phase shift Half-Tone

Polarization orthogonal spillover

• Grating layers: step and flash lithography (UTA).

• Freedom in image vs mask maker and equipment vendor bondage?

Phase-shifted spillover

Polarization adjusted half-tone

Polarization is a Remaining Frontier in Resolution Enhancement?

11/8/00

45

ASML Projection Printer: Tools and Status

• ASML Model 90: = 248 nm, NA = 0.52, = flex– Donated by ASML to Berkeley

– Cymer will assist with laser

– Asyst providing mask pods and opener

– VLSI donated SVG 8800 track

– Shipley, Shen-Etsu donated materials

• Status – Target Nov/Dec– Got it in Microlab! 7,000 lb, taller than ceilings

– Track out for retrofitting

– Room and gas handling underway

– Masks: make 0.4 m, buy some 0.3 m, make some PSM

11/8/00

46

ASML Projection Printer: Research and Synergism

• Image Quality– Aberrations including chromatic

– Pattern dependent resolution enhancement

– Polarization as a remaining degree of freedom

• Resist Modeling and Line End Shortening– Resist parameters including diffusion

– Role of resist and image quality on performance

• Ultimate Resist Limits– Line edge roughness

– Novel processing (Electric-field enhanced)

– Novel materials (dendrimers)

11/8/00

47

Results and Plans: Novel Technologies

• Novel Optical Lithography Enhancements – Electric-Field Enhanced Diff. – 3BEAMS M. Cheng

• Field Strength, Polarity, Frequency, Resist Type– Defect-Probe Aberr. Targets– BACUS A. Neureuther

• Tune sensitivity and orthogonality, Dry-lab, ASML verification

– Polarization nanoimprinted masks• Simulate near fields and images, Dry-lab, ASML verification

• Timetable and Milestones– Y1 Initial experiments and simulations to test concepts– Y2 Conduct and quantitatively interpret prototypes– Y3 Design apparatus, test procedures and data analysis

ASML Stepper Installation and Experiments

11/8/00

48

Results and Plans: Modeling and Simulation• Simulation of Lithography Performance

– OPC EM Analysis – SPIE K. Adam– 3D Phase-Defect EM Analysis – 3BEAMS K. Adam

• Establish models; Assess impact of typical defects, • Invent alternative inspection approaches

– Line End Shortening• Rapid Algorithms for Resist – SPIE M. Cheng • Combine tool aberration and resist models• Substrate topography effects

• Timetable and Milestones– Y1 Determine importance of physical effects– Y2 Guidelines for 3D phase defects; Substrate in LES– Y2 Model defect inspection; Tool, resist, substrate LES

ASML Stepper Installation and Experiments

8:30 – 9:00 research and educational objectives / spanos

Documents