Stochastic modeling of EUV resists: simulation of the...

Stochastic modeling of EUV resists: simulation of the effects of absorption,

quantum yield and photoelectric blur on 18 nm contact hole performance

John J. Biafore, Alessandro Vaglio Pret, Mark D. Smith, David A. Blankenship, Trey S. Graves

IEUVI TWG Resist, Hiroshima, 10.2016

2 KLA-Tencor Confidential. Copyright KLA-Tencor Corporation. All rights reserved.

Stochastic resist effects = local, random variability in the after-develop image

Examples: patterns printed in chemically-amplified photoresist with EUV [1]

[1] P. De Bisschop, J. Biafore, A.V. Pret, ‘Stochastic effects in EUV Lithography,’ ICPST, 2016

• Observed as variability in printed image or failure in electronic devices (LER, LWR, LCDU, etc.)

• Two components:

• Optical: Photon Shot Noise (PSN)

• Uncertainty in the amount of energy incident upon the resist

• Produces well-known dose dependence of variability

• Physical-Chemical: Effects specific to photoresists

• Uncertainty (noise) in the amount of energy absorbed by the resist

• Spatial blurring of absorbed energy by the photoelectric effect

• Noise in the number and positions of released acids

• Diffusional blurring during bake (but well-controlled in modern systems)

• Poor dissolution contrast (but well-controlled in modern systems)

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0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

0 20 40 60 80 100 120

LCDU, 34 nm holes, 56 nm pitch, 0.25NA, 0.51/0.81 45° quasar, 60 nm FTLCDU, 34 nm holes, 56 nm pitch, 0.33NA, 0.42/0.62 45° quasar, 60 nm FT

Neisser, EUVL Symposium 2013

Stochastic effects are reproduced well by PROLITH stochastic simulation

1𝜎

𝐿𝐶

𝐷𝑈

,𝑛𝑚

𝑠𝑖𝑧𝑖𝑛𝑔 𝑑𝑜𝑠𝑒, 𝑚𝐽/𝑐𝑚2

PROLITH EUV stochastic simulation of 170 virtual resists

Hole LCDU 1-sigma vs. Incident Dose

Measurements of many actual EUV resists

LWR 3-sigma vs. Incident Dose

𝑠𝑖𝑧𝑖𝑛𝑔 𝑑𝑜𝑠𝑒, 𝑚𝐽/𝑐𝑚2

L𝑊

𝑅,n

m

De Bisschop, JPS 2016

Smith, Biafore, SPIE2013

• Optical and physical-chemical effects conspire to produce feature variability

• Effects are modeled well by PROLITH stochastic simulation

• Optical effects: Well-known 1/ 𝑑𝑜𝑠𝑒 dependence is a consequence of PSN

• Physical-Chemical effects: Much variability observed among samples at fixed dose


Simulated effect of dose on hole LCDU 4 virtual resists, 24 nm holes, 48 nm mask pitch, EUV, 0.33 NA, 0.81/0.51 quasar

Hole diameter, nm Hole diameter, nm Hole diameter, nm Hole diameter, nm

2

2

Virtual EUV resist 1

16 /

1.7 1

1.57 acids/photon

D 0.047 / sec

0.0071

6.61/

quencher =

size

Acid

E mJ cm

LCDU nm

nm

k

m

PDB

2

2


24.5 /

1.4 1

1.77 acids/photon

D 0.056 / sec

0.0056

5.21/

quencher =

size

Acid

E mJ cm

LCDU nm

nm

k

m

PDB

2

2


44.5 /

0.9 1

1.72acids/photon

D 0.0185 / sec

0.0061

5.67 /

quencher =

size

Acid

E mJ cm

LCDU nm

nm

k

m

PDB

2

2


81 /

0.7 1

1.44acids/photon

D 0.136 / sec

0.0052

4.84 /

quencher = conventional

size

Acid

E mJ cm

LCDU nm

nm

k

m

𝑬𝒔𝒊𝒛𝒆 = 𝟏𝟔 𝒎𝑱/𝒄𝒎𝟐 𝑬𝒔𝒊𝒛𝒆 = 𝟐𝟒. 𝟓 𝒎𝑱/𝒄𝒎𝟐 𝑬𝒔𝒊𝒛𝒆 = 𝟒𝟒. 𝟓 𝒎𝑱/𝒄𝒎𝟐 𝑬𝒔𝒊𝒛𝒆 = 𝟖𝟏 𝒎𝑱/𝒄𝒎𝟐

𝑳𝑪𝑫𝑼 = 𝟏. 𝟕 𝒏𝒎 𝟏𝝈 𝑳𝑪𝑫𝑼 = 𝟏. 𝟒 𝒏𝒎 𝟏𝝈 𝑳𝑪𝑫𝑼 = 𝟎. 𝟗 𝒏𝒎 𝟏𝝈 𝑳𝑪𝑫𝑼 = 𝟎. 𝟕 𝒏𝒎 𝟏𝝈

• Reduction in variability can be achieved by ‘simply’ increasing dose, but very painful


Status of IMEC Low-Dose EUV CAR Resist Project, as of 10.5.16

IMEC conclusions:

• Resolution-roughness-sensitivity targets for the project are difficult to meet

• More than 300 exposures with 5 different suppliers performed over 7 months

• Few resists have shown acceptable printability

• All suppliers are able to formulate low-dose resists

• LWR target the most difficult to achieve

• All resists are in the LWR range of 5-6nm vs. target of < 3.2nm (20% of CD!)

16 nm dense line

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Only way to repair PSN in resist (without raising dose) is to increase absorption

𝜶 = 6.5/um

𝜶 = 18.6/um

𝑭𝑻 = 30 nm

𝑭𝑻 = 50 nm

• Low EUV absorbance by organic resists increases PSN effects and wastes incident energy

• But very difficult to increase EUV absorption in organic resists

• Reducing organic resist thickness is also problematic

• Optical density of a CAR is about 0.20 at 30 nm FT (0.80 of photons pass through)

• Target optical density is closer to 0.45-0.50

𝑃𝑆𝑁 =1

𝑛 𝑎𝑏𝑠=

1

𝜶 𝑬𝒊𝒏𝒄 𝑽𝝀

ℎ𝑐


1. Direct photolysis: 1 absorbed photon releases at most 1 acid

Photon absorption by the generating molecule produces an electronically-excited state

favorable for conversion. [2] A. Reiser, ‘Photoreactive Polymers,’ Wiley, 1989

[3] N. Turro, ‘Modern Molecular Photochemistry,’ University Science Press, 1991

2. Electronic excitation: 1 scattering electron may release > 1 acid

Absorption of an EUV photon results in ionization and a cascade of secondary electrons.

Scattering electrons induce time-dependent electric fields whose individual Fourier

components may be treated using the method of virtual quanta. If a resonating system is

located within the field, it may interact with the passing charge. [4] E. Fermi, 1924

[5] Weizsacker, Williams, 1934

[6] J. D. Jackson, ‘Classical Electrodynamics’, Wiley, pp. 724-729, 1975

[7] A. Reiser, ‘Photoreactive Polymers,’ Wiley, 1989

[8] G. Han, F. Cerrina, ‘Energy transfer between electrons and photoresist,’ JVST B18(6), 3297, 2000

[9] (as applied to EUV resists) Mack, Thackeray, Biafore, Smith, ‘Stochastic Exposure Kinetics of EUV Photoresists: A Simulation

Study,’ SPIE 2011

3. Dissociative electron attachment: 1 scattering electron may release at most 1 acid

• Absorption of an EUV photon results in ionization and a cascade of secondary electrons.

The dissociative‐attachment peak cross section is a function of peak resonance energy [8]. [10] Christophorou, Stockdale, ‘Dissociative Electron Attachment to Molecules,’ JCP, 48-5, 1967

[11] (as applied to EUV resists) Kozawa et al, J. Vac. Sci. Technology. B22, 2004 3489

Supported EUV exposure mechanisms

• Simulation of these exposure mechanisms is supported by PROLITH


Electrons

PROLITH ™

Acids

PROLITH ™

Visualization of the photoelectric exposure blur in PROLITH X6

Discrete Acid PSF in XY plane

Discrete Electron PSF in XY plane

ℎ𝜈

ℎ𝜈

e

ℎ𝜈

𝑃𝑆𝑁 = 𝜎𝑛

𝑛=

0

1= 0

~60 𝑛𝑚

• Absorb 1 EUV photon at the center of a cube of resist

• Perform simulation of stochastic exposure within cube

• Record the number and positions of acids released

• Record the number and positions of electrons at 10 eV

• Re-initialize cubic domain

• Repeat for a specified number of randomized trials

• PSN component is zero – why?

• In each trial, we absorb 1 photon with probability = 100%

• Standard deviation of n absorbed photons is therefore 0

• PSN vanishes and the approach models a pure resist effect


Simulation of material-specific electron scattering properties in PROLITH X6

Resist electronic response characterization via dielectric function measurement [12-15]

• The dielectric (or loss) function ε(ω,0) is calculated by measuring the

extinction coefficient κ(ω) and the real refractive index n(ω).

• To cover a large enough range of energies, 3 techniques are used:

• CXRO website for n(E), κ(E) in the 1nm < λ < 41nm range[11]

• Spectroscopic ellipsometry for n(E), κ(E) in the 150nm < λ < 750nm range

• XAS, for n(E), κ(E) in the 4.5nm < λ < 450nm range (@ Elettra synchrotron)[12]

• Electron mean free path, CDSA energy loss and screening parameter can

be modeled from ε(ω,0)

• Scattering data can be loaded in PROLITH X6 to simulate the resist’s

electronic response to absorption of high energy photons [13-22].

• XAS can be also used to estimate other material properties such as Total

and Secondary Electron Yield (TEY, SEY), and used in PROLITH X6 [12] J. Biafore et al. “Pattern prediction in EUV resists”, Proc. SPIE 7520 (2009)

[13] A. Vaglio Pret et al. “Characterization and modeling electrical response to light for metal based EUV photoresists, Proc. SPIE 9779-5 (2015)

[14] P. De Schepper et al. XAS Photoresists Electron/Quantum yields study with synchrotron Light, Proc. SPIE 9425-6 (2015)

[15] CXRO website: http://www.cxro.lbl.gov/

[16] A. Giglia et al. “FEL multilayer optics damaged by multiple laser shots: experimental results and discussion”, Nucl. Instr. Meth. Phys. Res. A. 635,

530 (2011).

[17] J. C. Ashley et al. “Interaction of low-energy electrons with condensed matter stopping powers and inelastic mean free paths from optical data”, J.

Elec. Spectr. 46 (1988)

[18] M. Dapor et al. “Monte Carlo modeling in the low energy domain of the secondary electron emission of PMMA for CD-SEM”, JM3 9, (2010)

[19] D. R. Penn. “Electron mean-free-path calculations using a model dielectric function”, Phys. Rev. B, 35, (1987)

[20] V. P. Singh, et al. “Determination of Effective Atomic Numbers Using Different Methods for Some Low-Z Materials”, J. Nuc. Chem. (2014)

[21] H. Bethe, “Zur Theorie des Durchgangs schneller Korpuskularstrahlen durch Materie”, Annalen der Physik, 397(3), 325-400, (1930).

[22] M. Born, "Quantenmechanik der Stossvorgänge". Zeitschrift für Physik 38(11-12), 803-827, (1926).

[23] T. Schnattinger, “An Introduction to the Passage of Energetic Particles through Matter”, Taylor & Francis Group, LLC, CRC Press, Boca Raton,

(2007).

http://www.cxro.lbl.gov/


Details of the following stochastic simulation experiment

• PROLITH X6, EUV, 0.33 NA, 0.862 / 0.60 30° quasar, no aberrations, 3.5% flare, 3D mask model

• 18 nm contact holes, 36 nm mask pitch, 22.36 nm mask opening

• Build 8 virtual resists based on a state-of-the-art calibrated resist model

• Each virtual sample has different properties

• Simulate 10,000 contact holes using each sample

• Dose = 35 mJ/cm2 for all samples, unless noted

• Quencher loading is modified to adjust sizing of each virtual resist

• Absorbance coefficient ~ 5/um, unless noted

• Resist thickness = 30 nm

• All other parameters are identical for all samples and describe high-performance EUV CARs:

• Low PEB acid diffusion length ~ 2.5 nm

• Steep dissolution contrast with low Rmin 0.005 nm/s and high Rmax 120 nm/s

• Moderate-low amplification reaction rate constant 0.42 nm3/s

• High quenching rate constant 4 nm3/s, conventional quencher

Collect simulation output:

• 3σ hole CD

• Failure rate, number closed holes / 10,000

• Acid quantum yield, electron quantum yield

• Displacement of acids from the absorption site

• Displacement of electrons <= 10 eV from the absorption site


3𝜎 𝐶𝐷 = 6.3 𝑛𝑚

𝑓𝑎𝑖𝑙 𝑟𝑎𝑡𝑒 = 0.0061

Acids

PROLITH ™

Electrons

PROLITH ™

Φ = 2.9 𝑎𝑐𝑖𝑑𝑠/ℎ𝜈

𝑟 = 3.8 𝑛𝑚

Φ = 3.8 𝑒𝑙𝑒𝑐𝑡𝑟𝑜𝑛𝑠/ℎ𝜈

𝑟 = 4.4 𝑛𝑚

𝐴𝑐𝑖𝑑 𝑃𝑆𝐹

𝐸𝑙𝑒𝑐𝑡𝑟𝑜𝑛 𝑃𝑆𝐹 𝑎𝑡 10𝑒𝑉

𝑟𝑚𝑎𝑥 = 12.3 𝑛𝑚


Sample 1: Control, based on calibrated model of a modern state-of-the-art CAR

• Distribution of CD is non-normal likely due to nonlinearity of underexposure latitude

Emulation of SEM image


Electrons

PROLITH ™

Acids

PROLITH ™

Electrons

PROLITH ™

𝑆𝑎𝑚𝑝𝑙𝑒 2 3𝜎 𝐶𝐷 = 𝟗. 𝟑 𝒏𝒎

𝑓𝑎𝑖𝑙 𝑟𝑎𝑡𝑒 = 𝟎. 𝟏𝟑𝟏

𝚽 = 𝟏𝟏. 𝟑 𝐚𝐜𝐢𝐝𝐬/𝐡𝛎

𝑟 = 3. 0 𝑛𝑚


𝑟 = 3.6 𝑛𝑚



𝑟𝑚𝑎𝑥 = 16 𝑛𝑚


Sample 2: Extreme quantum yield using direct excitation mechanism only

• No benefit is observed for very high quantum yield, even if it were possible

𝐶𝑜𝑛𝑡𝑟𝑜𝑙 3𝜎 𝐶𝐷 = 6.3 𝑛𝑚


2.9

3.8 𝑛𝑚

12.3 𝑛𝑚

3.8

4.4 𝑛𝑚

42.5 𝑛𝑚

𝐶𝑜𝑛𝑡𝑟𝑜𝑙 Emulation of SEM image Acids

PROLITH ™


Electrons

PROLITH ™

Acids

PROLITH ™

Electrons

PROLITH ™


𝑟 = 1.9 𝑛𝑚


𝑟 = 2.3 𝑛𝑚





𝑆𝑎𝑚𝑝𝑙𝑒 3 3𝜎 𝐶𝐷 = 𝟔. 𝟎 𝒏𝒎

𝑓𝑎𝑖𝑙 𝑟𝑎𝑡𝑒 = 𝟎. 𝟎𝟎𝟑𝟔

Sample 3: Controlled exposure blur and moderate quantum yield

• Reduction of acid exposure blur produces better CDU and 1/2 the

failure rate of the control. Acid yield less than control.




2.9

3.8 𝑛𝑚

12.3 𝑛𝑚

3.8

4.4 𝑛𝑚

42.5 𝑛𝑚

𝐶𝑜𝑛𝑡𝑟𝑜𝑙 Acids

PROLITH ™


Electrons

PROLITH ™𝑆𝑎𝑚𝑝𝑙𝑒 4

3𝜎 𝐶𝐷 = 𝟓. 𝟏 𝒏𝒎

𝑓𝑎𝑖𝑙 𝑟𝑎𝑡𝑒 = 𝟎. 𝟎𝟎𝟎𝟑

𝜶 ≈ 𝟏𝟎/𝝁𝒎

Acids

PROLITH ™

Electrons

PROLITH ™


𝑟 = 1.9 𝑛𝑚


𝑟 = 2.2 𝑛𝑚





• Reduction of acid exposure blur & higher absorption produces better

CDU and 20x less failures versus control.



𝛼 ≈ 5/𝜇𝑚


2.9

3.8 𝑛𝑚

12.3 𝑛𝑚

3.8

4.35 𝑛𝑚

42.5 𝑛𝑚


PROLITH ™

Sample 4: Controlled exposure blur, moderate quantum yield and higher absorption


Electrons

PROLITH ™

𝑆𝑎𝑚𝑝𝑙𝑒 5 𝟑𝝈 𝑪𝑫 = 𝟑. 𝟗 𝒏𝒎

𝒇𝒂𝒊𝒍 𝒓𝒂𝒕𝒆 = 𝟏/𝟏𝟖𝟎𝑲

𝒅𝒐𝒔𝒆 = 𝟕𝟎 𝒎𝑱/𝒄𝒎𝟐

𝜶 ≈ 𝟏𝟎 /𝝁𝒎

Acids

PROLITH ™

Electrons

PROLITH ™


𝑟 = 1.8 𝑛𝑚

Φ = 2.4/ℎ𝜈

𝑟 = 2.2 𝑛𝑚




𝑟𝑚𝑎𝑥 = 32 𝑛𝑚

𝐶 = 0.044 𝑐𝑚2

𝑚𝐽

• Reduction of acid blur, increased absorbance, 70 mJ/cm2 dose and

modest yield gives low CDU and failure rate of 0 over 30000 trials.



𝑑𝑜𝑠𝑒 = 35 𝑚𝐽/𝑐𝑚2

𝛼 ≈ 5/𝜇𝑚


2.9

3.8 𝑛𝑚

12.3 𝑛𝑚

3.8

4.4 𝑛𝑚

42.5 𝑛𝑚


PROLITH ™

Sample 4: Controlled exposure blur, moderate yield, higher absorption, higher dose


Intermediate summary

Acids

PROLITH ™

1. CONTROL

6. CONTROL + HIGH [F] POLYMER

3. LOWER EXP. BLUR

8. CONTROL & METAL OX. ABSORPTION & e SCATTERING

4. LOWER EXP. BLUR

HIGHER ABSORPTION

5. LOWER EXP. BLUR

HIGHER ABSORPTION

HIGHER DOSE

1. CONTROL

Acids

PROLITH ™

𝑨𝒗𝒆𝒓𝒂𝒈𝒆 𝒂𝒄𝒊𝒅 𝒆𝒙𝒑𝒐𝒔𝒖𝒓𝒆 𝒃𝒍𝒖𝒓 𝒓𝒂𝒅𝒊𝒖𝒔, 𝒏𝒎

𝑯𝑶𝑳𝑬 𝑭𝑨𝑰𝑳 𝑹𝑨𝑻𝑬

4. LOWER EXP. BLUR

HIGHER ABSORPTION

𝑟 = 3.8 𝑛𝑚

𝑟 = 1.9 𝑛𝑚

• Feature failure rate complements CDU and its reduction is obviously of utmost concern

• So far, failure rate improves with:

• Higher absorption (maximize absorbed photons to minimize PSN effects)

• Smaller photoelectric acid blur (avoid degradation of the image)

• Modest acid yield (information transfer is via photons and noise associated with

stochastic acid generation mechanism only makes image worse)

0.000006


Electrons

PROLITH ™

Electrons

PROLITH ™

Acids

PROLITH ™

𝑆𝑎𝑚𝑝𝑙𝑒 6

3𝜎 𝐶𝐷 = 𝟗. 𝟐 𝒏𝒎

𝑓𝑎𝑖𝑙 𝑟𝑎𝑡𝑒 = 𝟎. 𝟏𝟓𝟓

𝜶 ≈ 𝟐. 𝟒 /𝝁𝒎

𝚽 = 𝟏. 𝟏 𝐚𝐜𝐢𝐝𝐬/𝐡𝛎

𝑟 = 2.3 𝑛𝑚

𝚽 = 𝟏. 𝟔/𝐡𝛎

𝑟 = 2.0 𝑛𝑚





Sample 6: Control with PHS polymer

Using PHS-specific electron scattering model

• Low absorption polymer = high PSN effects and catastrophic failure

Φ = 1.5 measured



𝛼 ≈ 5/𝜇𝑚


2.9

3.8 𝑛𝑚

12.3 𝑛𝑚

3.8

4.35 𝑛𝑚

42.5 𝑛𝑚


PROLITH ™


Electrons

PROLITH ™

Electrons

PROLITH ™

Acids

PROLITH ™

𝑆𝑎𝑚𝑝𝑙𝑒 7 3𝜎 𝐶𝐷 = 𝟓. 𝟔 𝒏𝒎

𝑓𝑎𝑖𝑙 𝑟𝑎𝑡𝑒 = 𝟎. 𝟎𝟎𝟓𝟏

𝜶 ≈ 𝟓 +/𝝁𝒎


𝑟 = 2.3 𝑛𝑚

Φ = 3.4/ℎ𝜈

𝑟 = 1.9 𝑛𝑚





• Higher absorption vs. PHS, better control of acid exposure blur vs.

control, better CDU and 20% less failures vs. control

Sample 7: Control with highly fluorinated polymer

Using high [F] polymer electron scattering model

Φ = 4.2 measured




2.9

3.8 𝑛𝑚

12.3 𝑛𝑚

3.8

4.35 𝑛𝑚

42.5 𝑛𝑚


PROLITH ™


Electrons

PROLITH ™

Electrons

PROLITH ™

Acids

PROLITH ™

𝑆𝑎𝑚𝑝𝑙𝑒 8 3𝜎 𝐶𝐷 = 𝟓. 𝟎 𝒏𝒎

𝑓𝑎𝑖𝑙 𝑟𝑎𝑡𝑒 = 𝟎. 𝟎𝟎𝟐𝟔

𝜶 ≈ 𝟏𝟗 /𝝁𝒎

Φ = 2 acids/hν

𝑟 = 1.8 𝑛𝑚

Φ = 2.4/hν

𝑟 = 1. 1𝑛𝑚





Sample 8: Control with metal-containing additives

Using metal-oxide specific electron scattering model

Φ = 2.3 measured



𝛼 ≈ 5/𝜇𝑚

• Electron scattering and acid blur are well-controlled. Very high

absorption. Better CDU and 42% less failures vs. control


2.9

3.8 𝑛𝑚

12.3 𝑛𝑚

3.8

4.35 𝑛𝑚

42.5 𝑛𝑚


PROLITH ™


Summary

Acids

PROLITH ™

1. CONTROL

7. CONTROL + HIGH [F] POLYMER

3. LOWER EXP. BLUR

8. CONTROL & METAL OX. ABSORPTION & e SCATTERING

4. LOWER EXP. BLUR

HIGHER ABSORPTION

5. LOWER EXP. BLUR

HIGHER ABSORPTION

HIGHER DOSE

1. CONTROL

Acids

PROLITH ™

𝑨𝒗𝒆𝒓𝒂𝒈𝒆 𝒂𝒄𝒊𝒅 𝒆𝒙𝒑𝒐𝒔𝒖𝒓𝒆 𝒃𝒍𝒖𝒓 𝒓𝒂𝒅𝒊𝒖𝒔, 𝒏𝒎

𝑯𝑶𝑳𝑬 𝑭𝑨𝑰𝑳 𝑹𝑨𝑻𝑬

4. LOWER EXP. BLUR

HIGHER ABSORPTION

𝑟 = 3.8 𝑛𝑚

𝑟 = 1.9 𝑛𝑚

• Clear by now that reduction of stochastics is a difficult challenge for EUV lithography

• Minimization of feature failure rate is of utmost concern

• Failure rate improves with:

• Higher absorption (minimize PSN effects)

• Smaller acid exposure blur (avoid image degradation upon exposure)

• Modest yield (less noise associated with stochastic acid generation mechanism)

• Low PEB blur, steep dissolution contrast are known requirements

0.000006


CNR/Elettra

Angelo Giglia

IAP

Marco Ornigotti

IMEC

Kurt Ronse, Geert Vandenberghe, Peter De Bisschop

Inpria

Peter De Schepper, Mike Kocsis, Jason Stowers

JSR

Kenji Hoshiko, Hori Masafumi, Dei Satoshi

KLA-Tencor

Antonio Mani, Roel Gronheid

NIMS

Shigeo Tanuma

NIST

Cedric J. Powell

Acknowledgements


Two measurements of acid quantum yield vs. PAG loading at EUV in CARs

• Quantum yield, Φ, is the number of acids produced per absorbed photon

• Two different experiments yet qualitatively similar results

• For the majority of the acid produced, the yield saturates with PAG loading

Kozawa et al ICPST, 6.2011 Higgins et al 2011


Simulated yield using a simplified electronic excitation mechanism in PROLITH

Kozawa, Tagawa, ICPST, 6.2011

Potentially high yield

No saturation

• Energy loss process is uniform along the electron trajectory and is described by the loss function.

• The passing charge induces a time-dependent electric field upon the system (PAG) whose Fourier

components represent ‘virtual photons’ [3,4]

• Electron is not trapped by the PAG

• One electron may convert multiple PAGs and simulated yields can be very large (unphysical)

• Trend is roughly linear with PAG loading and no saturation is observed

• Excitation mechanism alone doesn’t fully explain data


Simulated yield using a simplified electron trapping mechanism in PROLITH

Yield limited

Saturation

Kozawa, Tagawa, ICPST, 6.2011

• Electron may be trapped by the struck system

• The dissociative‐attachment peak cross section is a function of peak resonance energy [7]

• One electron may convert at most one PAG

• Yields is limited by the number of electrons

• Trend is nonlinear with PAG loading

• Saturation observed in model but max yield less than data; doesn’t fully explain data


Modeling quantum yield vs. loading for different PAGs

Higgins 2011 [9]

• Calibration of exposure simulation parameters

using both the direct excitation and electron trapping

mechanisms produces a good match to data for

each PAG tested by Higgins

[ ]( )PAG mM

Biafore, Smith, 2012 [10]

[9] C. Higgins, ‘EUV Photoresists: Film Quantum Yield and LER …,’ Dissertation, Univ. Albany, NY, CNSE, 2011

[10] Biafore, Smith, ‘Application of stochastic simulation to resist optimization,’ SPIE 8325-17, 2012

Lines = PROLITH

Points = Data

Φ

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