“A Better Turnkey Solution for Particle Monitoring!”
In the previous article, Smart Factory: Particle Monitoring Part I, we described the importance of particle monitoring in a cleanroom environment and how critical it is for the manufacturing environments of automotive, display panel, pharmaceutical and semiconductor industries. And this article is going to explain you why MtM+’s cost effective PSN device and particle monitoring solution is especially designed for this manufacturing application scenario.
The following Figure-1 table summarizes the features of MtM+’s particle sensing node. MtM+’s solution supports Class 100 and higher.
“Prerequisite of the Particle Sensor, Quality Matters!”
MtM+’s Smart Factory solution provides an innovative approach with cutting edge technology to remotely measure particles in various critical location spots by strategically installing particle sensing nodes (PSN) throughout the clean environment in a mesh networking style. And the quality of PSN particle sensors need to be strictly assured by following our standard screening approach in order to meet the requirement of Class100/1000 cleanroom.
Each PSN is equipped with state-of-the-art sensors that count particles with the light scattering method. It typically can detect particles sizes from 0.3 µm to 10 µm with a counting efficiency of 50% (+/-20%) at 0.3um.
Screening flow as Figure-2 below is applied at the sensor’s IQC stage to ensure there’s enough sensitivity for the cleanroom application requiring ISO Class 1000 & higher.
The quality of the purposed sensor parts will be first screened by the performance check in a “Isolated Chamber” to simulate the cleanroom environment. After passing the comparison between each sensor and MetOne’s high-end particle counter, then the known good parts will be sent to assembly line.
“Screening of the particle sensor’s sensing capability!”
Sensing capability is the most important part for the particle count sensor in cleanroom applications. We conduct group test to preliminarily exclude these sensors with inadequate sensing capability for sensing 0.3 μm or 0.5μm particles in the experimental chamber.
Figure-3 is a comparison a group of particle sensors that will be used for MtM+ mesh PSNs after screening test.
“On-site Calibration for Better Accuracy!”
On-site calibration procedure will be followed to reach better accuracy after finishing the assembly of our Mesh PSN device. Figure-4 below shows our standard operation procedure flow after screening stage. The specific environment and calibration methods will be further explained next.
“Regression Analysis – A Powerful Method for Modeling Data.”
In order to reach better accuracy, we adopt the egression analysis in our calibration method to eliminate systematic difference between manual monitoring (standard approach) and automatic monitoring (MtM+’s approach) and thus to reach better data consistency.
Our data calibration principle for automatic particle monitoring is made with reference to US Environmental Protection Agency (EPA) in according to US Code of Federal Regulation (CFR). CFR provides the calibration method which establish the linear regression in the non-federal reference method monitoring
And Taiwan’s government department, EPA (Environmental Protection Administration, Executive Yuan), has also defined a calibration principle of automatic particle monitoring according to US Federal Regulations from EPA.
Figure-5 shows one example of our sensor test result using regression analysis based on this calibration method mentioned above.
We use calibration tool to find out the gain and offset used in regression analysis and the estimated RMSE (root-mean-square-error). The final result of the calibration method applied needs to pass the 1-min mode and 10-mins mode in both given stable and unstable environment. Figure-6 below shows the UI of this tool.
The Figure-7 and Figure-8 below are examples of test results of our sensors in unstable and stable environments before/after the calibration. The black curve is the measurement result of a standard lab meter, and the red curve in the plot is the result of our sensor. The RMSE value in each mode needs to be lower than the upper limit in order to pass our calibration procedure.
“Benchmarks of Systematic Error…”
Deviation between particle counters is investigated and PSN’s numeric calibration tolerance is defined accordingly. If the staff takes the particle number in 1min to forecast that in 10 mins, the cumulative deviation between different meters can be obtained. The systematic errors between MetOne devices and PSNs can be found in the Figure-9 below.
“A More Reliable Inspection Method.”
It’s hardly to obtain the overall picture of a cleanroom’s cleanliness level by using manual sampling measurement. However, MtM+’s mesh PSN network can help you monitor the cleanliness per minute within 24 hours with multiple PSN nodes deployed in scattered locations to eliminate blind spots.
The pass ratio of a cleanroom is not always 100%, few samples per day could lead to wrong perception and misleading conclusion. Figure-10 below shows the particle counting results of 4 different devices and one can see the overall data and trend curve for each day and even zoom in to investigate the data details on a minute-to-minute basis.
“Fast Deployment is the Key!”
Wireless mesh PSN network provides customers more advantages than you think. Fast deployment is the key for users to proceed with PoC (Proof of Concept) and more efficiently to estimate the ROI of this turnkey solution without spending too much cost on wiring and too much time on scheduling for stopping the production line and breaking the wall for installation. Figure-11 is an example of our floor plan for the PoC of MtM+ PSNs Installation. Customer can quickly experiment with this turnkey solution and evaluate its performance in the production line with 15 pcs of PSN devices only.
MtM+’s smart factory solution on particle monitoring has been deployed in a semiconductor manufacturing company with great results. Big data generated with these PSN boxes are vital for analysis in improving factory flow, floorplan and process. The keys to success are strategic placement of each sensors along with MtM+’s calibration algorithm.
“We need your Feedback!”
Real-time data are also essential to prevent possible disasters before it’s too late. For more information on this technology and MtM+’s offerings, please email email@example.com.