738885_en-US

liliconcom

Gauge R&R of X-ray Photoelectron Spectroscopy to Monitor a Coating Process - (2023-US-30MP-1450)

X-ray photoelectron spectroscopy (XPS) analyzes the surface chemistry of materials. It is also known as electron spectroscopy for chemical analysis (ESCA) and is commonly used to measure elemental composition/stoichiometry of thin film coatings in different industries. We have applied this technique to develop Atomic Layer Deposition (ALD) AlOx coating (of < 50 nm thick) processes by testing deposited film composition to identify the O/Al ratio.

Our pharmaceutical program customers questioned whether XPS is an appropriate metrology technique to detect process variation in the coating composition. This presentation demonstrates the adequacy of XPS by using Gauge R&R in JMP 17. We designed our testing experiments using measurement systems analysis (MSA) designs platform, and a fast replicate crossed model was used with six sample coupons on two different kinds of substrates (silicon coupons and active pharmaceutical ingredient [API] pellets). Each sample was split into four parts and all 24 (6 X 4) samples were measured independently by two different vendors blindly.

The data distribution was reviewed using a variety of methods: X-bar and R control chart, performed repeatability, reproducibility, part-to-part variation testing, calculated Gauge R&R (P/TV), P/PV, P/T in MSA Gauge R&R platform. Also, the evaluating the measurement process (EMP) platform was used to determine interclass correlation (ICC) and to identify if any interaction exists with either substrate type or vendor. Both MSA platforms confirmed that part variation is significantly higher than precision level, hence XPS is adequate to detect the variation in the process

Hello .

Good morning , everyone . Good evening , everyone .

I 'm Sukti Chatterjee .

Before starting my presentation ,

I would like to introduce myself with few words .

I 'm Sukti Chatterjee from CTO team of Applied Materials .

It is advanced technology team , and our team goal is to develop product

adjacent to the semi industry or outside the semi industry .

For example , we are working for aerospace industry ,

pharmaceutical industry , or industrial coating .

This example , present example ,

we are taking from the pharmaceutical industry .

My topic of the presentation today.

Gauge R&R of X -ray photoelectron spectroscopy to monitor a coating process .

Agenda of my talk today.

Fi,rst, we will talk about the background and problem statement ,

then we will discuss about the operation definition and data collection plan .

Next , MSA component analysis .

Finally we 'll talk about the plan for MSA component improvement .

Let 's start with the background .

In the pharmaceutical industry , in therapeutic windows , therapeutic areas ,

there are , for example , antibiotic drug , alcohol addiction or cancer patient ,

they need everyday injection because drug level in the blood

is certainly increasing , spiking in the blood ,

and then within short time , within a few hours ,

it is going beyond the therapeutic window limit .

That 's why they need everyday injection and it is painful .

It causes some side effect ,

that 's why patient skipping the medication or stop the medications .

To solve this problem ,

our customer needs some approach to tailor the release of drug .

Our team developed a barrier layer , aluminum oxide barrier layer ,

that forms a shell around the pharmaceutical particles .

Properties of this barrier layer

can control the release of the drug in the blood .

Even it is possible like that ,

it can release few weeks instead of few hours .

Here , we will talk about the composition analysis ,

and what is the noise analysis of this composition measurement ?

That we will talk here .

Our problem is measurement of AlOx coating composition .

Our spec limit , customer spec limit , is O /Al ratio

in the aluminum oxide film is 1 .2 -2 .3 .

Our objective here to determine the XPS method if it is adequate

to differentiate AlOx process variation .

We will determine here , gauge R&R measurement error

of XPS for AlOx composition analysis .

X -ray photoelectron spectroscopy can measure quantitatively

atomic percentage of composition .

It can measure aluminum and oxygen percentage .

XPS actually measure the kinetic energy

of photoelectrons emitted from the elements

and it counts the electrons .

Whenever it is counting the electrons , it can count the presence of elements

and also it counts the element which is bond to it .

That 's why we can get the information

about aluminum and oxygen in the aluminum oxide film .

Most of the source of error for XPS , it can add it in gauge R&R .

It can reproducibility , it 's coming from the calibration electron count .

It can add repeatability and reproducibility error .

Analysis can add reproducibility error .

We will talk more details this one in the next slide .

In our operation definition , we 'll talk about the different steps

of the XPS measurement and how it can introduce

the error in the measurement error GRR , gauge R&R error .

Our objective measure aluminum oxide coating composition ,

and to measure it in XPS , first we need to do baseline correction .

It is automatic , and then we need to go to the calibration .

In calibration , normally applied materials have calibration sample ,

especially whenever we have some

developed technologies like aluminum oxide .

But in our cases , we are coating pharma particles

and our process window is totally different

from our applied materials core technology process window for aluminum oxide ,

because coating need to be compatible with the pharma particles .

We are coating this particle , at the same time ,

we are coating also silicon wafer and API pallet

because XPS cannot measure particles .

It needs some planar substrate .

That 's why we are depositing on silicon wafer and API pallet .

Since we don 't have calibration sample , we are using the second option

for calibration like carbon peak calibration .

Left -hand side picture , you can see carbon peak calibration

and it is manually need to do it,

and that 's why it impact on the reproducibility .

Then after calibration , we need to do XPS survey

or high resolution scan to get the spectra.

In the spectra , you can see oxygen peak, aluminum peak .

Since we need to do manual calibration and we have automatic baseline correction ,

this can impact error on repeatability and reproducibility .

Next , we need to do analysis . Analysis is peak fitting .

We need to fit this peak and then we can gauge .

From peak area , we can calculate the oxygen aluminum percentage .

Since it is semi automatic , it can add error in the reproducibility .

By XPS measurement , we are calculating O /Al ratio

and our customer spec limit is 1 .2 -2 .3 .

Next , we will talk about the cause and effect diagram ,

MSA cause and effect diagram .

In MSA cause and effect diagram , we did some detailed analysis,

and we found several one it can impact on the gauge R&R .

We highlighted also major ones like electron counts ,

calibration analysis , we talked in the earlier slide .

Now we 're adding another one , it 's sample loading ,

how it is added error in the gauge R&R .

Sample loading , we need to do it . It 's not automatic , it is not full wafer .

We are doing with coupon wafer , so we need to place the coupon .

If it is location a little bit different or angle is little bit different ,

then it can impact on the measurement .

This is impacting on reproducibility .

All other major one impact we already discussed in the previous slides .

Other one is the sample . It depends on the process .

For this presentation , it is out of scope .

We will talk about these four in this presentation .

Next , our sample collection plan .

For our sample collection , we use six samples for MSA analysis ,

and for these six samples we have four replicates .

Here you can see these four replicates .

We measure those samples in two sites .

Since we have the four replicates , we are measuring those sequentially .

Is it possible that if samples are degraded

then sample degradation could be a risk ?

We will talk about this risk later on more details .

Our expected outcomes like that we need to find out XPS method

is adequate to differentiate process variation .

Also , you like to gauge like that

whenever we are measuring two sites that have similar result .

Also , we like to gauge like that sample or part is not interacting with the site .

Now we need to do the MSA design .

In MSA design , we are using substrate and site at the cost factor .

This is the site , this is the part , and we have also two different substrate .

We mentioned it before , API palette and silicon wafer .

S numbers are silicon wafer , A numbers are API palette .

We 'll not be able to use actually completely randomized option

and we use first repeat .

For first repeat option ,

here we are not changing the sample replicate number .

That could impact on sample degradation problem .

That 's why later on we will compare first and fourth replicate

to check this sampling risk .

For this MSA analysis ,

we sequentially use several JMP applications from JMP platform .

We use data distribution of MSA samples .

That is from descriptive inferential statistics application .

It is from distribution fit Y by X .

Then we check the data variability using control chart and one -way ANOVA .

Then we analyze gauge R&R components .

It is from the variability chart .

Then we 'd like to gauge like that what is the relation

with process capability with gauge R&R .

That we can find out like that , interclass correlation versus P /T plot .

Next , we did the root cause analysis to plan for improving the GRR .

We will find out what is the GRR major error

and how we can find out .

That we are using for box plot , density ellipse ,

matched pairs , and fit line

that are different platform of JMP platform .

Let 's start with the data distribution .

We developed process initially at the two spec limit ,

upper spec limit and lower spec limit .

In upper spec limit , we have two samples , two parts and four replicates .

All are measured two sites ,

and we already mentioned we did the first repeat .

Similarly , at the lower spec limit , also we have four parts ,

four replicates and two sites .

Since we did the process development at the two end of the spec limit ,

that 's why we can see that our distribution is bimodal .

It's completely bimodal distribution .

Problem of bimodal distribution , it can impact on the GRR components .

It can impact on P /TV ratio ,

it can impact on P /PV ratio and misclassification .

Since P /T ratio is not related with ...

It is not dependent with the part , that is the reason P /T ratio

it 's not impacting by the sample distribution .

That 's why we will be used in our following slides .

Our figure of merits we are using as a P /T ratio .

For misclassification probabilities , there is five probabilities .

Last three , it could be impacted by the sample distribution more ,

and first two is less impacted .

To minimize the risk , again ,

we are focusing on the P /T ratio as a figure of merit .

In the next time , our plan to do MSA analysis

using uniform sample distribution .

Let 's check now the variability of data .

Here we can see that we use I -MR chart ,

individual moving range chart , and we saw that many data points

are outside the control limit in the upper chart ,

and in the lower moving range chart ,

we saw that three data point is outside the control limit ,

and that these three data points , it is sudden shift .

It is sudden shift , it 's not staying there , it is going back .

It means it is the type II shift

and there is a mixture of common cause variation

and special cause variation in the control chart .

That 's the reason here control limits are meaningless .

We need to subgrouping with special cause

and then only we can consider the control limits .

Now we like to find out what are the special cause .

First we will check if part variation could be a special cause .

We did it using the one -way ANOVA and in one -way ANOVA ,

we can see there is a variation of the samples .

We did the process near upper spec limit and we did the process lower spec limit .

That 's why samples are different .

That also we found by one -way ANOVA , and here we can see that

within variation is very small compared to part variation ,

and also by analysis of variance is showing like that .

Here our hypothesis is all parts are same ,

but it is rejecting the hypothesis because P -value is less than 0 .05 .

It 's telling us it is significantly different .

That means part variation is a special cause ,

so we can use as a candidate for subgrouping .

Again , similarly we check with the site variation if it is a special cause or not .

We considering two sites measurement near upper spec limit

as well as near lower spec limit .

We saw that here our hypothesis is two sites are measurement similar,

and we found that its P -value is higher than 0 .05 .

For upper spec limit ,

there is no evidence that we can reject the hypothesis .

It is similar , on the other hand , for lower specs limit .

It is marginally rejected because it is less than 0 .05 .

For site variation , either it is marginally rejected

or there is no evidence to reject .

That 's why site variation is not a good candidate

and part variation is the better candidate .

What we did next , we make our control chart again with phase option and A here ,

sampled part at a different phase .

When we do it , we saw that in a moving range chart ,

we found change in the variation in the measurement in the moving range ,

and that calculated the control limits for the bottom chart and the upper chart .

Now we saw that all the points ,

all the measurement points are inside the control limit .

These is the variations of each sample . It is the repeatability .

When we consider site A and site B ,

and we saw also site B has also repeatability .

But compared to site A and site B , there is some variation of repeatability .

That is called reproducibility .

Now we calculate the gauge R&R , all the components in the next slide ,

and we 'll find out what is the dominating error in gauge R&R .

First , we did main effect .

We didn 't consider for the main effect part and site variation interaction ,

so only the main effect .

Here , we saw the repeatability , reproducibility .

Repeatability is 22 % and reproducibility is 15 %.

I already mentioned as a gauge R&R , we are considering P /T ratio

because our sample distribution is bimodal ,

and we saw that P /T ratio is 26 %.

It is passed , it is less than 30 %.

It is marginally passed , and major error is 22 % repeatability .

One more thing I should mention here , we are considering P /T ratio

but P /TV or P /PV ratio is very close for our measurement cases

because our sample distribution is bimodal and at the two end of the spec limit .

That is the reason this ratio T or TV are very close or PV is very close .

That is the reason we have this gauge R&R .

This figure of merits is very close .

Also , I should mention here type I error alpha and type II error beta .

Type I error , all our data points within the control limit .

That 's the reason our type I error good part is falsely rejected .

It 's very small . It is less than 6 %.

On the other hand , type II error , it is 6 %, it is failed .

It is more than 10 %.

Why type II error is higher ?

Our repeatability is the major issue .

Whenever we are measuring the samples , it is within the spec limit .

But it is possible like that whenever a customer is measuring it .

It could be beyond the spec limit because repeatability is high here .

At this point , since we are developing the product ,

we are in the initial feasibility check phase .

Customer is happy with this beta type II error ,

but we have option .

If we can improve the repeatability , then it can improve this part also .

On the other hand , if we can consider that part and site interaction ,

then we saw that part and site interaction is 6 %,

not that much ,

but there is a little bit interaction.

And when we didn 't consider the interaction in the main effect mode ,

then this interaction is added in the repeatability .

That 's why we found that whenever we are considering the crossed effect ,

we saw repeatability little bit decreasing

because our interaction is very small , not that much decreasing .

Since this interaction is very small ,

our figure of merits are not changing that much .

It is changing from little bit .

Now from here ,

we know that our dominating error is repeatability .

Before going about the more discussion with repeatability ,

first another thing I would like to mention ,

process capability with gauge R&R .

Effect of gauge R&R on the process capability .

Here , process capability we are plotting in ICC versus P /T plot .

ICC is the part variation to total variation

and P /T is the six sigma gauge , and USL minus LSL .

We calculated from here Cₚ , and in our cases ,

in our process current condition , Cₚ is 0 .93 .

It is less than one .

It is in the red zone , and we need to go Cₚ ...

For a good process capability , we need to go between Cₚ 1 .33 -2 .

It is the yellow zone .

To improve this Cₚ , what we need to do ?

In this part , this is the process part

and in this direction , it is the measurement part .

Process variability or part variability is very high .

For our measurement , we saw that our P /T is 24 %.

If we would like to increase , if we would like to improve the P /T

from 24 to suppose 15 % or 10 %, then we have to improve 30 % -50 %,

and within that , our repeatability is the main issue .

That is the reason we need to improve the repeatability .

Now it is question .

If we need to improve the repeatability , do we need to change our measurement tool ?

That is again depending on the ROI that is question to our managing level ,

or we can address the repeatability in different way .

That 's why we 'd like to find out the root cause why repeatability is higher .

Here we are considering variability chart with analysis of variance .

Here we can see that we plot all the samples variability together

with site A and site B measurement .

You can see that suppose , for a sample A0 ,

this is the measurement repeatability , and it is changing .

This repeatability is changing for all the parts .

Also repeatability is changing with the site to site

because here you can see repeatability is 0 .06 standard deviation ,

but in these cases when they measure their repeatability is 0 .03 .

That is the reason this repeatability is changing

with part to part also site to site .

Whenever it is changing with site to site ,

it 's called reproducibility .

Here if you can consider the analysis of variance ,

then we can see that site to site variation is much smaller

than within variation .

This is the repeatability , within variation , and site to site .

Site to site variation , it is reproducibility , it 's much smaller .

Repeatability again from here also we find out that it is the bigger problem .

Now in the next to find out the root cause ,

we plotted all the repeatability side by side together ,

and for both the cases , USL , upper spec limit and lower spec limit ,

and all the cases we found that its repeatability is different .

Next we like to correlate or find out any relationship

if it is present site A and site B measurement .

Ideally , site A measurement will equal to site B measurement should be .

But in our cases , we did some linear fit and we found that we have intercept

as well as we have linear fit slope , it is not one , it is not zero .

Here we found that linear slope is less than 0 .4

and intercept is higher than 0 .9 .

Our fitting points are distributed widely .

That 's the reason our R -squared is also poor .

We also did the density ellipse

and density ellipse also telling that this correlation is less than 0 .5 .

If they have a very good correlation relationship ,

then it should be 0 .9 .

If it is 0 .6 , then it will be moderately correlated .

But in our cases , it 's not that .

That 's why we know that site A measurement is not site B measurement .

It 's the repeatability impacted on the reproducibility .

Problem of repeatability is impacted on the reproducibility .

Now we check more closely how it is different .

We are comparing by match pair the site A and site B variation .

Here our hypothesis is site A equal to site B ,

that means site A minus site B equal to zero .

We saw that our probability for this hypothesis ,

site A minus site B equal to zero , is less than 0 .05 in both the cases .

It is upper spec limit and lower spec .

Both the cases you can see that it is probability is less than 0 .05 .

That means site A and site B measurement is different ,

and you can see our difference of mean value

and confidence interval is above the zero point line .

That means though this is

site A measurement is always higher for site B measurement .

Now from here , our question appears , since we did the first repeat analysis

for our MSA design is first repeat , it could be possible like that

if samples are degraded , like O /Al composition is degraded .

That 's why we did again match pair test

with first and fourth measurement

both in site A and site B for all six samples ,

and we found that here ,

first measurement minus fourth measurement equals zero .

That is our hypothesis .

We saw that P -value is higher than 0 .05 both the cases .

That means our sample degradation is not an issue .

First sample , there is no evidence .

First measurement and fourth measurement is dissimilar .

That means it is the measurement issue .

For that , this is summarized in the dashboard table ,

in the dashboard , like our figure of merit

for gauge R&R 24 % and repeatability is 21 %

and that repeatability is changing from part to part and site to site ,

and we have always higher repeatability for site A compared to the site B .

Now for our next plan ,

we plan for a discussion each site as well as with the process team .

Site has a problem like repeatability as well part -site interaction .

We know that what error could be introduced in the measurement

like background /baseline correction , electron counts , peak deconvolution .

We 'll discuss those methods source of error with site A person ,

site A facility , and we will find out

how we can do the streamlining process for improve our MSA .

Also we have a plan set up a calibration sample

or we can set up a set up sample

that we can measure in regular interval in the both sites .

On the other hand , with the process team , we 'll talk to improve MSA next time

to MSA data collection uniform .

Instead of bimodal , we should collect the data uniformly .

Then also we saw part to part repeatability variation .

There is one reason it could be measurement issue .

Another reason could be process is not uniform .

We need to validate our thermal math to check our process uniformity .

Finally , I would like to mention that what is the impact

on my learning for this MSA analysis .

Now we know that several JMP platform or JMP application can help me to know

what is the signal variation from the noise variation ,

and then we can identify what figure of merit we can use

to justify our measurement method .

In our cases , we found P /T is the best method ,

best figure of merit to analyze it .

Then how misclassification risk can relate to the MSA component

as well as sample distribution that we learn .

Root cause analysis , we did several JMP application

that can help us to plan to improving MSA .

Since it is very helpful for particular program application ,

that 's why I would like to introduce

this data driven decision making for all the programs I involve in

to improve the project quality , cost , and time .

Finally , I would like to promote data driven decision using JMP

in our advanced technology group like CTO team ,

or other different projects .

This is my final slide .

I would like to mention my journey .

I started JMP learning beginning of the year ,

and that time we did A0 , A1 , A2 . This is my foundation .

Then after I work with MSA analysis and SPC .

I also got my certificate , JMP STIPS certificate May 2023 .

Now I am instructor at AMAT JMP instructor .

I 'm planning to in person presentation in October 2023 ,

and also I am working for my Black Belt on 2024 .

Thank you for listening .

Advanced Statistical ModelingBasic Data Analysis and ModelingConsumer and Market ResearchData Blending and CleanupData Exploration and VisualizationDesign of ExperimentsPredictive Modeling and Machine LearningQuality and Process EngineeringReliability Analysis