© IMEC 2010 / CONFIDENTIAL Optimizing high frequency ultrasound cleaning in the semiconductor industry Steven Brems

© IMEC 2010 / CONFIDENTIAL

Optimizing high frequency ultrasound cleaning in the semiconductor industry

Steven Brems


Outline

▸ Introduction to particle removal

▸ Improving state-of-the-art megasonic cleaning- Acoustic pulsing- Oversaturated liquids- Traveling waves

▸ Future of particle removal with liquid motion in the semiconductor industry

▸ Conclusions

2


Introduction: Particle cleaning

▸ Nanoparticle removal with pure chemical cleaning is only effective if >2 nm material is removed.

▸ A combination of physical and chemical cleaning methods will become more important

Particle attached to

wafer surface

Lift-off from surface:

repulsive forces(electrostatic: z)

Breaking of the Van der Waals

forces(under)etching

Transport away fromsurface: diffusion,

convection

Mechanism of particle removal by pure chemical cleaning

vF

3

200 nm

20 nm


Outline




▸ Conclusions

4


Towards a control of bubble size: Pulsing

▸ At sufficiently high gas concentration and acoustic pressures, bubbles can grow by rectified diffusion and bubble coalescence

▸ Microbubbles (< 4 mm) will always shrink when ultrasound is turned off and dissolved gas saturation is below 130%- Bubbles could kept around resonance radius by turning the acoustic

field on (bubbles grow) and off (bubbles dissolve)

J. Lee et al., JACS 127, 16810 (2005)

Pulse on time Pulse off timeperiod Pulse

on time Pulse (DC) CycleDuty

5


In-situ measuring micro-bubble activity

oscilloscope

amplifier

Hydrophone

Wafer Transduce

r

Example of cavitation noise spectra

▸ Bubble oscillation- Frequency distribution of the oscillating bubble motion can contain harmonics, subharmonics and

ultraharmonics

The components arise from the nonlinear motion of a bubble acoustic emission

▸ Non-integer harmonics (5f0/2, 7f0/2, 9f0/2…) :- Particular characteristic of non-linear (stable) bubble motion

Can be used as an indicator for bubble activity

▸ Strong (transient) cavitation produces white noise (increase of background signal)- Instable cavitation = damaging cavitation

6


0 450 900 1350 1800

-80

-75

-70

-65

-60

7/2 ultraharmonic 9/2 ultraharmonic

Ultr

ahar

mon

ic s

igna

l (dB

V)

Pulse off time (ms)0 300 600 900 1200

-80

-75

-70

-65

-60

0 200 400 600

-80

-75

-70

-65

-600 50 100 150 200 0 100 200 300 400

Pulse on time (ms)0 200 400 600

Cavitation noise spectra: Influence of pulses

▸ Experimental details- Oxygen concentration: 120 %, applied power: 640 mW/cm2

- Duty Cycle is varied▸ Optimal pulse off time (indicated with ) is independent of duty

cycle variation▸ Bubble activity decreases with increasing duty cycle

▸ However, a lower DC also means a lower effective cleaning time!7

-8dB=40%

DC 10% DC 25% DC 50%


Understanding of optimal pulse off time

Dissolved oxygen

concentration 120%

~ resonant bubble size

Dissolution time resonant

bubble

The dissolution time of a resonant bubble lies very

close to the optimal experimental determined

pulse off time

Bubble size

‘reservoir’

Lost bubbles

Dissolution during pulse-off time

Growing to active size during pulse-

on time

Production of new bubbles (transient collapse, shape

instabilities)

Bubble size distribution centered around resonance radius

Inactive bubbles that continue to grow or active bubbles that grow

out of resonance

8


Cavitation Activity: Role of On-Time

0 200 400 600 800 1000

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Ultr

ah

arm

on

ic c

avi

tatio

n s

ign

al (

a.u

.)

Pulse-off time [ms]

10 ms 50 ms 250 ms

0 500 10000.0

0.1

0.2

0.3

0.4

0.5

Pulse-off time [ms]

Cavitation noise data

9

Pulse on times

▸ A simple bubble model based on bubble growth, bubble loss and bubble creation mechanisms can model the pulse on time variation.- A maximum bubble activity is

reached with a pulse on time of ~50 ms

0 200 400 600 800 1000

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Ultr

ah

arm

on

ic c

avi

tatio

n s

ign

al (

a.u

.)

Half integer harmonics Fit

Pulse-On time [ms]

tgrow= 8.6 ms

teff =1.1 s

Pulse on time variation at constant pulse off time (150 ms) and 105 %

dissolved gas

Bubble size

Reservoir Lost bubbles


Continuous 125 ms 150 ms 175 ms

0.42 W/cm2

0.25 W/cm2

Influence of pulse off time

10

PRE maps for variable pulse off times, a fixed pulse on time (50 ms) and a dissolved oxygen concentration of 105%

Acoustic field 145 mm from transducer surface▸ Non-uniform acoustic field is a

near-field (interference) effect caused by the transducer size.

▸ Non-uniform fields result in localized cleaning.

Experiment Simulation

Acoustic pulsing noticeably improves particle removal without changing

acoustic power densities

PR

E (

%)

0

100

50


Outline




▸ Conclusions

11


Maximazing bubble formation

12

Bubble formation is limiting the megasonic cleaning efficiency.▸ An increased dissolved gas concentration facilitates the nucleation of

bubbles

90% 100% 110% 120% 125% 130%

Impossible to nucleate bubbles

Bubbles do not dissolve anymore

PRE as function of dissolved oxygen concentration

Duty cycle is 10%, pulse off time is optimized for dissolved gas concentrations and applied power is 420 mW/cm2.

The optimal dissolved gas concentration facilitates bubble formation ( ≥ 100%) and enables bubble

dissolution ( < 130%)

PR

E (

%)

0

100

50


Bubble dissolution or growth in the absence of an acoustic field is given by

100 120 140 160 180

0

10

20

30

Bu

bb

le r

ad

ius

(m

)

Dissolved oxygen gas (%)

Dissolution

Upper limit dissolved gas concentration

13

Bubble resonance size

00

1

000

0

00

3

41

11

P

P

C

C

RPDtR

R

TCDR

dt

dR gig

This term determines bubble growth or dissolution

Growth


Outline



▸ Benchmarking of physical cleaning techniques


▸ Conclusions

14


▸ Standing wave field- Bubbles experience an acoustic radiation force (Bjerkness force):

At moderate acoustic powers, bubbles smaller (larger) than resonance size will travel up (down) a pressure gradient. So small bubbles go to pressure antinodes and large bubbles go to pressure nodes.

▸ Traveling wave- To simulate bubble motion in a traveling wave, acoustic radiation force, added

mass force (inertia) and viscous drag force need to be taken into account. As a result, radial and translational equations are coupled.

Increasing PRE: transport of bubbles towards the wafer surface

15

pVF

95 96 97 98 99 1000

1

2

Radial oscillation

R(t

) /

R0

time [Ac. Cyc.]

0.265

0.270

0.275

0.280

0.285 z-position

Po

sitio

n [

mm

]

Simulation of a 2.7 mm sized bubble (radius) in an acoustic field of 0.73 W/cm2. The average bubble velocities is in the order of m/s.


Influence of a traveling wave on particle removal efficiency

Wafer

Damping material

Transducer

▸ A silicon wafer is transparent for acoustic waves at a specific angle

▸ With the combination of damping material, a traveling wave can be formed- Bubbles are transported towards

the wafer surface and improve particle removal

16


Outline




▸ Conclusions

17


Large particles

18

Small particles

200 nm

100 nm

Boundary layer thickness >> 100 nm

Although the removal force increases for larger particles, it gets easier to remove large particles because drag force scales with radius and velocity

30 nm

100 nm

A structure with a high aspect ratio gets problematic, due to a strong increase in drag force on that structure

Physical cleaning techniques based on a fluid flow are ideally suited to remove ‘larger’

particles.

Particle cleaning with liquid motion


Conclusions

▸ System optimization- Experimental megasonic system is optimized

Controlling average bubble size with acoustic pulsing Facilitating bubble nucleation with slightly oversaturated

liquid Transporting bubbles towards wafer surface with traveling

waves

▸ Challenges- Megasonic cleaning uniformity needs to be solved- Cleaning of 30 nm and smaller silica particles with low

damage levels is not yet achieved Boundary layer and aspect ratio of structures makes

current techniques not suitable for continued scaling

19


Acknowledgements

Thanks to

▸ Marc Hauptmann, Elisabeth Camerotto, Antoine Pacco, Geert Doumen, Stefan De Gendt, Marc Heyns, Geert Doumen and Tae-Gon Kim (Imec)

▸ Christ Glorieux (KULeuven)

▸ Aaldert Zijlstra (University of Twente)

20ANTOINE PACCO

Documents

© IMEC 2010 / CONFIDENTIAL Optimizing high frequency ultrasound cleaning in the semiconductor industry Steven Brems