public - high tech nl€¦ · nxe euv design rule: 1 um 100 nm 10 nm +- 0.1 um +- 10 nm +- 1nm...

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Tom Hoogenboom, Marcel Beems

apc|m conference 2018

April 18, 2018 | Dresden, Germany

Lithography control is data hungry

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ASML

int. ref. D607280

http://tce-eu.asml.com/getpdf.html?D000607280

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ASML is a patterning/lithography company


Clever sampling strategies reduce metrology time

Machine learning is one of our tools

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Summary

Objectives

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Summary

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It’s hard to imagine a world without chipsSlide 4

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A global presence

Veldhoven

Wilton (CT)

Chandler (AZ)

Korea

Taiwan

San Diego (CA)

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A global presence

4,300 employees

Source: ASML Full Year 2017

Offices in over 60 cities in 16 countries worldwide

10,500 employees 4,300 employees

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Big data company - summarized

19,299 collegues

16 countries

~20 customers

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0.1

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2004Total

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2012Total

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2014Total

2015Est.Total

Total NAND flash units [B]

NAND Flash price 1 GB [$]

Reduction of cost per bit drives market growthPublic

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hard drivesSmart

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Smaller chips, bigger machines

1980s:

PAS 2000/5000

1990s:

PAS 5500

2000s:

TWINSCAN

2010s:

NXE EUV

Design rule:

1 um 100 nm 10 nm

+- 0.1 um +- 10 nm +- 1nm

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Summary

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2015TWINSCAN NXT:1980Di

Our most advanced

immersion system

1984PAS 2000

ASML’s first stepper

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Keeping up with Moore’s Law

2017TWINSCAN NXE:3400B

High volume

EUV system

Wavelength:

13.5 nanometer

Resolution:

≤ 22 nanometer

Overlay:

1.0 nanometer*

Wafer size:

300 mm Ø

Productivity:

125 wafers per hour*

*) use case dependent

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2015TWINSCAN NXT:1980Di

Our most advanced

immersion system

1984PAS 2000

ASML’s first stepper

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Keeping up with Moore’s Law

2017TWINSCAN NXE:3400B

High volume

EUV system

Wavelength:

13.5 nanometer

Resolution:

≤ 22 nanometer

Overlay:

1.0 nanometer

Wafer size:

300 mm Ø

Productivity:

125 wafers per hour

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Control at nm level is really fine-grained

1 mm motion

on scale of Saxony (300 km Ø)

1:300,000,000

1 nm motion

on scale of wafer (300 mm Ø)

1:300,000,000

300 km

↔ 1 mm

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Control at nm level is really fine-grained

1 mm motion

on scale of Saxony (300 km Ø)

1:300,000,000

1 nm motion

on scale of wafer (300 mm Ø)

1:300,000,000

300 km

↔ 1 mm

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nm control is based on lots and lots of data

Up to 300 wafers per hour,

> million per year per machine

Lithography: 10,000 samples per wafer

TeraBytes of data per year

Metrology tool: 100-10,000 samples per wafer


We use this to control every square mm2 of the wafer (300 mm Ø):

100,000 inputs, 6 axis = 600,000 inputs/wfr

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nm control is based on lots and lots of data

Up to 300 wafers per hour,

> million per year per machine

Lithography: 10,000 samples per wafer


Metrology tool: 100-10,000 samples per wafer


We use this to control every square mm2 of the wafer (300 mm Ø):

100,000 inputs, 6 axis = 600,000 inputs/wfr

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Introduction ASML ApplicationsDeliver performance and control through holistic approach

Ensure stability

during HVM production

Ensure optimal

yield by design

Ensure metrology

efficiency and effectiveness

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Summary

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Before control:

• Metrology for verification

• After the fact

Metrology for control:

• Dense sampling is ideal but takes time and has a cost

• Sparse sampling may not capture every variation

• Clever combination of sparse+dense is optimal

Example: dense metrology

Example: sparse metrology

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Before control:


• After the fact


• Dense sampling is ideal but takes time, has a cost and takes real-estate




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Before control:


• After the fact







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Before control:


• After the fact







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One strategy is to separate dense and sparse in time

Dense Sparse

Capture stable

fingerprint at

intervals

Capture

lot to lot

changes more

frequently

Combine dense and sparse to

estimate total variation

to feedback/feedforward

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A more optimal strategy takes the best of both:introducing SSO (Sampling Scheme Optimization)

Approach:

• Start with the dense sampling plan

• Remove samples until just those remain

that best represent the source(s) of variation:

Dense SSO (200pts)

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A A more optimal strategy takes the best of both:introducing SSO (Sampling Scheme Optimization)

Approach:



that best represent the source of variation:

Dense SSO (200pts)

• All 200 samples

estimate x-y

‘translation’

• Edge samples

estimate

‘magnification’

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A more optimal strategy takes the best of both:introducing SSO (Sampling Scheme Optimization)

Approach:



that best represent the source of variation.

• Result is still close to the

‘true’ values measured using

the dense plan.

• Measurement time is reduced.

• The 200 pts in the example

can be measured in-line

reducing cycle time.

SSO (200pts)

• All 200 samples

estimate x-y

‘translation’

• Edge samples

estimate

‘magnification’

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Two more optimizations are possible

Same results as all-dense sampling are possible:

1) running dense at intervals,combining with more frequent sparse sampling

2) running an optimal subset of sparse and dense (SSO)

Two ways to further optimize knowledge about patterning variations:

3) Bring in additional metrology data to interpolate the missing dense points

• E.g. from sensors in the lithography tool

4) Separate detection from control

• fast measurements locate sites in need of control

• more accurate but slower metrology determines the control move

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Concept: add metrology data from scanner to compute

wafer maps for non-measured wafers (ref: presentation Paul Böcker)

After-develop metrology

few wafers only

Scanner metrology

per wafer

On product wafers:

Process Fingerprint

On product wafers:

Metrology maps for 100% of the wafers

Computational metrology maps

for 100% of the wafers

Sampled wafers Sampled wafers

|

All wafers

+ ||

All wafers

processing

Alignment fingerprints

Leveling maps

Servo maps

Lens distortion coefficients

Monitoring (wafer) maps

focus / overlay map

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Separate detection from control:optical metrology preselects areas for e-beam metrology

optical

Design data

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Separate detection from control:optical metrology preselects areas for e-beam metrology

optical

Design data

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Summary

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Even with ‘big data’ approach clever sampling is needed

The 4 ways to arrange sampling for control, reducing metrology time:

1) run dense at intervals, combine with more frequent sparse sampling

2) run an optimal subset of sparse and dense (SSO)

3) bring in additional metrology to interpolate missing data

4) separate detection from control

Still not good for ‘big data’ approach:

- The most prominent ‘feature’ is the choice of sampling plan:

- So we need to further condition the input data

parameters = data not available (75%)= data available (25%)

time

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Why even bother about ‘big data’?

“Big” data and multivariate relationships cannot be handled

by a human analyst/domain expert.

Data science/machine learning sciences provide powerful and

efficient methods to demonstrate relationships,

without physical understanding.

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Some thoughts on industrialization

In a well-controlled industrial

process >99% of the data is

within limits.

Only outliers are potentially

interesting: scarce data after

all…

Big data, really? Slide 36

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Some thoughts on industrialization

In a well-controlled industrial

process >99% of the data is

within limits.

Only outliers are potentially

interesting: scarce data after

all…

We use ‘big data’ algorithms to

help find off-spec wafers

Big data, really? Slide 37

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Use case: Group wafers by clamped wafer shape

The Lithography machine clamps a wafer on a stage.

This deforms the surface (at nm scale).

Groups of wafers show similar behavior.

To spot an off-spec wafer, it is useful to group the wafers first,

because scanner sensor data is related to the shape ‘as clamped’

Use case for machine learning:

- Group wafers by their shape ‘as clamped’

- Find out-of-spec wafers by comparing with average wafer in group

based on the samples from scanner alignment / UVLS (UV level sensor)

Approach [1]*:

. Train a deep feedforward neural network to measured overlay metrology

The training can be done based on a dense sample plan.

. Compare new wafers using neural network classify as in/out-of-spec

*) see next slide for SPIE reference

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Reference for this topic

[1] Full paper presented at SPIE 2017:

Emil Schmitt-Weaver, Venky Subramony, Zakir Ullah, Masazumi

Matsunobu, Ravin Somasundaram, Joel Thomas, Linmiao

Zhang, Klaus Thul, Kaustuve Bhattacharyya, Ronald Goossens,

Cees Lambregts, Wim Tel, Chris de Ruiter,

"Computational overlay metrology with adaptive data analytics“,

Proc. SPIE 10145, Metrology, Inspection, and Process Control

for Microlithography XXXI, 101450V (28 March 2017).

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[1]

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Recap

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public - high tech nl€¦ · nxe euv design rule: 1 um 100 nm 10 nm +- 0.1 um +- 10 nm +- 1nm...

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