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Correlating Particle Counter Data Between Different Instruments Mike Naggar MGN International Inc. May 2018 ULTRAPUREMICRO2018.COM 1 Purpose Who / Where: Anyone that uses a particle counter system: filter and membrane manufacturers,

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Correlating Particle Counter Data Between Different Instruments

Mike Naggar MGN International Inc. May 2018

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Purpose

  • Who / Where:
  • Anyone that uses a particle counter system: filter and membrane manufacturers, container

and fitting makers, UPW and facilities, chemical and photo-chemical suppliers, semiconductor Fabs, contamination control, parts cleaning, QA/QC, etc.

  • What:
  • How to correlate and compare data between different particle counter models.
  • When:
  • Before, during, and after the switch to a different particle counter model, design, or

technology.

  • Comparing particle counters with different specifications.
  • Why:
  • Traceability and correlation back to full exposure (100%) light scattering particle counter.
  • Establish correlation of new instrument to historical data: instrument qualification,

define/update control limits, product specifications, utilization of historical SPC data.

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Outline

  • Basics of Light Scattering Particle Counter
  • Challenges of Detecting Smaller Particle Sizes
  • Counting Efficiency, Flowrate, and Effective Flowrate (View Volume)
  • Example Design Changes to Improve Signal Intensity
  • Counting Efficiency Across the Dynamic Range
  • How to Normalize Data for Comparison
  • Example of Data Normalization Across Different Models
  • Calculation examples and data graphs
  • Measurement precision / repeatability / stability
  • Correlation overlap across different models (same manufacturer)
  • Conclusions / Summary
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Science of Light Scattering Particle Counter

  • Mie’s Theory of light scattering
  • Rayleigh’s Theory of light scattering

   

2 1 1 1 1

cos cos 2 1 ( 1) sin P dP i a b d

    

      

 

                 

   

2 1 1 2 1

cos cos 2 1 ( 1) sin P dP i b a d

    

      

 

                 

 

 

 

 

 

 

 

 

' ' ' ' p p p p p p

m m m a m m m

        

                  

 

 

 

 

 

 

 

 

' ' ' ' p p p p p p

m m m b m m m

        

                  

 

1 cos

: P Legendre polynomial

, : Bessel function

 

 

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Design of Light Scattering Particle Counter

  • 3 Core Components
  • Light Source (Laser)
  • Detection Zone / Flowcell
  • Detector

Time Signal (voltage) 0.5mm 0.3mm 1.0mm

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Challenges of Detecting Smaller Particles

  • Exponential decrease of particle signal as physical size decreases
  • Scatter Intensity ∝Particle Size ^6
  • Ex: If particle size decreases by half, the scatter intensity drops to 1/64
  • Elimination of background noise to improve accuracy / repeatability
  • Electronic, chemical matrix, polymers, surfactants, contamination, etc.
  • Efforts to increase particle signal also increases background noise
  • Separation between particle signal and background noise (S/N ratio)
  • Need to increase particle signal while simultaneously decrease background

noise

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Counting Efficiency, Actual Flowrate, Effective Flowrate

  • Counting Efficiency
  • The percentage of total sample volume that is measurable at a particular particle size
  • 100% counting efficiency – 100% of the sample liquid passing through the sensor is measurable
  • 1% counting efficiency – 1% of the sample liquid passing through the sensor is measurable
  • May vary from size channel to size channel and instrument to instrument.
  • Dependent on technology, design, and engineering of each instrument
  • Independent of the actual flowrate
  • Actual Flowrate / Actual Volume
  • The total sample flowrate or total sample volume through the sensor
  • 10 mL/min into the sensor
  • 1,000 mL/min into the sensor
  • Independent of counting efficiency
  • Effective Flowrate / View Volume
  • Effective Flowrate = (Actual Flowrate)(Counting Efficiency)
  • View Volume = (Actual Volume)(Counting Efficiency)
  • 10 mL/min at 100% efficiency = 10 mL/min effective flowrate; or 10 mL View Volume in 1 min.
  • 1,000 mL/min at 1% efficiency = 10 mL/min effective flowrate; or 10 mL View Volume in 1 min.
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Design Example to Increase Particle Signal

  • Below designs and specifications are based on liquid particle counters from RION CO., LTD. (Japan)

0.2µm 0.1µm 0.05µm 0.03µm

~ Signal Compared to 0.2µm N/A 1 64 1 4,096 1 87,791 Laser Wavelength 780 nm 830 nm 532 nm 532 nm Laser Output Power 40 mW 200 mW 500 mW 800 mW Counting Efficiency 100% 70% 10% 5% Data Example 100 in Reads 100 100 in Reads 70 100 in Reads 10 100 in Reads 5

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  • Depending on the design, there could be different counting efficiencies at different

particle sizes across the dynamic range

  • Flat / even laser intensity distribution = same / similar counting efficiencies
  • Distributed / uneven laser intensity distribution = different counting efficiencies
  • Consult the particle counter manufacturer for the counting efficiencies at the particle

sizes for comparison

Counting Efficiency Across the Dynamic Range

* Courtesy of RION CO., LTD. (Japan)

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Counting Efficiency Across the Dynamic Range

0.05 0.2

Particle Sizes Effective Flowrate

  • Need to know the counting efficiency across the dynamic range
  • Consult the particle counter manufacturer for the counting efficiencies at the particle sizes for comparison
  • Ideally, the counting efficiencies of a particle counter is substantially the same across the dynamic range
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How to Normalize Data for Comparison

  • Good Practices for Test Planning and Experiment Setup
  • Minimize variables
  • Batch sampling is preferred (control and consistency in sample)
  • Inline sampling can be done, but will require very precise particle injection system (SEMI-C-77-0912)
  • Measure samples from same bottle
  • Use same inlet / outlet lines
  • Use same flow control
  • Minimize disturbance to the sample and lines
  • Minimize contamination
  • Pre-rinse, flush, and baseline setup
  • Clean environment (cleanroom, laminar flow hood, clean zone systems)
  • Gloves, masks, etc.
  • Avoid touching the liquid contacting portions of the inlet tubing
  • Minimize the time between sampling
  • Run all tests in one setting
  • Poly dispersed sample solution
  • Ex: Drop(s) of city water into bottle of filtered DIW
  • Measures all channels at once with one sample
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How to Normalize Data for Comparison

  • What You Need:
  • Raw data
  • Counting efficiencies at each particle size for comparison
  • Actual flowrate
  • Measurement time
  • Determine Sample Volume:

Sample Volume 𝑛𝑀 = Actual Flowrate 𝑛𝑀 𝑢 × Measurement Time (𝑢)

  • How to Normalize Data for Comparison (to 100%)

Normalized 𝐷𝑝𝑣𝑜𝑢𝑡 𝑛𝑀 = Raw Data 𝐷𝑝𝑣𝑜𝑢𝑡 ÷ SampleVolume 𝑛𝑀 ÷ Counting Efficiency (𝑣𝑜𝑗𝑢𝑚𝑓𝑡𝑡)

  • How to Normalize Data for Comparison (from unit A to unit B with different counting efficiency)
  • This method is used to minimize the normalization factor(s) used and/or when both A & B have low counting efficiency

Normalized to B 𝐷𝑝𝑣𝑜𝑢𝑡 𝑛𝑀 = Raw Data A 𝐷𝑝𝑣𝑜𝑢𝑡 𝑛𝑀 × Counting Efficiency B Counting Efficiency A

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How to Normalize Data for Comparison: Examples

Normalized 𝐷𝑝𝑣𝑜𝑢𝑡 𝑛𝑀 = Raw Data 𝐷𝑝𝑣𝑜𝑢𝑡 ÷ SampleVolume 𝑛𝑀 ÷ Counting Efficiency (𝑣𝑜𝑗𝑢𝑚𝑓𝑡𝑡) Sample Volume 𝑛𝑀 = Actual Flowrate 𝑛𝑀 𝑢 × Measurement Time (𝑢)

Particle Size Raw Data Counting Efficiency Actual Flowrate Meas. Time Sample Volume Normalized Data 0.1um 100 counts 70% (0.7) 10mL/min 1 min 10 𝑛𝑀 𝑛𝑗𝑜 × 1𝑛𝑗𝑜 = 10 𝑛𝑀

100 𝑑𝑝𝑣𝑜𝑢𝑡 10 𝑛𝑀

0.7

= 14.3 𝑑𝑝𝑣𝑜𝑢𝑡/𝑛𝑀

0.1um 7 counts 5% (0.05) 10mL/min 1 min 10 𝑛𝑀 𝑛𝑗𝑜 × 1𝑛𝑗𝑜 = 10 𝑛𝑀

7 𝑑𝑝𝑣𝑜𝑢𝑡 10 𝑛𝑀

0.05

= 14.0 𝑑𝑝𝑣𝑜𝑢𝑡/𝑛𝑀

* Consult the particle counter manufacturer for counting efficiency

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How to Normalize Data for Comparison: Examples

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How to Normalize Data for Comparison: Examples

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How to Normalize Data for Comparison: Examples

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How to Normalize Data for Comparison: Examples

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How to Normalize Data for Comparison: Examples

10 100 1000 10000 100000 1000000 0.01 0.1 1

Particle Diameter（mm） Number of Particles（N/10mL）

Unit B (70%) Unit A (1%)

Unit A (1%) Unit B (70%)

Particle Numbers (/mL)

0.01 0.1 1 10 100 4 8 12 16 20

Time (hours)

0.05μm 0.1μm 0.1μm 0.15μm 0.2μm 0.3μm 0.5μm

DIW DIW

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How to Normalize Data for Comparison: Examples

A(1%) 0.06mm A(1%) 0.1mm B(70%) 0.1mm B(70%) 0.15mm B(70%)-0.2mm

Number of particles (mL-1) Time (hours)

H2SO4

  • All data is normalized for

comparison – Focus is 0.1um

  • Particle counters with the same

effective flowrate may show different repeatability

  • Need to look at both effective flow

rate AND counting efficiency

  • Two systems with the same

effective flowrate may appear to have the same specifications, however, higher counting efficiency system shows better repeatability / stability of data

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How to Normalize Data for Comparison: Examples

  • Normalized data can be used to check the correlation all the way back to a full exposure system
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Conclusions

  • Counting Efficiency ≠ Effective Flowrate (View Volume)
  • Higher Counting Efficiency = Better Precision (Repeatability)
  • Ability to properly correlate data between different systems allows:
  • Utilization of historical data
  • Step-wise correlation of partial detection systems back to full detection
  • Need to be able to correlate new technologies (beyond light

scattering) back to the established full exposure (100%) detection systems

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Discussions

  • Understanding the numbers
  • End-users – looking for events / trends
  • Sensitivity / precision
  • Filter makers – filter efficiency
  • Sensitivity / efficiency
  • Suppliers – specs / control limits / process control
  • Traceability / accuracy / precision
  • Understanding the correlation between the different instruments will

allow you to achieve the above

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Thank You!

Questions?

Mike Naggar MGN International Inc. May 2018

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Citation / References:

  • Rion Co., Ltd, http://www.rion.co.jp/english/product/particle/pdf/particle-E.pdf
  • Kaoru Kondo, Rion Co., Ltd, Latest Technology and Standardization Trends for Liquid-borne Particle Counters
  • Kaoru Kondo, Masaki Shimmura, and Takuya Tabuchi, Rion Co., Ltd., Measurement of Particles in Liquid

Materials Using the Light Scattering Method

  • SEMI C-77-0912. Test Method for Determining the Counting Efficiency of Liquid-Borne Particle Counters for

Which the Minimum Detectable Particle Size is Between 30 nm and 100 nm

  • Particle Measuring Systems, Advanced guide to particle technology
  • JIS B 9925, Light-scattering Liquid-borne Particle Counter
  • ISO 21501-2, Determination of particle size distribution - Single particle light interaction methods – Part2:

Light scattering liquid-borne particle counter

  • Rion, Co., Ltd, Instruction Manual Particle Sensor KS-19F
  • NIOSH 2014-002, Current Strategies for Engineering Controls in Nanomaterial Production and Downstream

Handling Processes

  • T. L. Engelhardt, C. Collins, A. Turner, Pacific Scientific Instruments Paper No. 58, Pressing Particle Counter

Sensitivity Lower for Improved Response

  • HACH, 28043_89, Analytical Procedures 2200 PCX Particle Counter Particle Counter Performance Verification