Smart Factory: Particle Monitoring Part II

“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.

Figure-1 MtM+ PSN’s Spec and Features

“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.

Figure-2 MtM+’s Screening Flow for the Sensitivity of PSN

“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.

Figure-3 Comparison among a group of particle sensors

“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.

Figure-4 On-site Calibration Procedure

“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.

Figure-5 Upper: Comparing METONE’s test result with our sensor’s calibrated one
Bottom: Linear Regression Formula and the curve result between METONE and our sensor

“Performance Benchmark!”

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. 

Figure-6 UI of Particle Sensor Calibration 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.

Figure-7 Sensor’s Particle Test Result in an Unstable Environment
Figure-8 Sensor’s Particle Test Result in a Stable Environment

“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.

Figure-9 Benchmarks of Systematic Errors.
Upper Plot: MetOne1 VS MetOne2. Bottom Plot: MetOne VS PSN

“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.

Figure-10 24/7 Historical Data Analysis in a mesh PSN way

“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.

Figure-11 Example of a real floor plan for PSN deployment

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

Smart Factory: Particle Monitoring Part I

“Every particle counts! Size really matters!”

The manufacturing environments of pharmaceutical and semiconductor have strict requirements for minimizing dust, airborne organisms or vaporized particles.  These isolated areas or rooms are referred to as a “Clean Room”.  A Class 1,000 clean room is equivalent to an ISO 6, which contains 102,000 particles that are bigger than 0.3 µm in size for each cubic meter.  However, a Class 100 clean room is often requirement for the manufacturing environment which can only contain 10,200 particles that are 0.3 µm in size or smaller per cubic meter!

There are several clean room standards, typically ISO 14644-1 and FED STD 209E.  The following Figure-1 table summarizes these standards and their equivalence.  MtM+’s solution supports Class 100 and higher.

Figure-1 Cleanroom classification and standardization

The existing industry solution to ensure a clean environment is to manually measure the number of particles in air using a portable meter (ie. MetONE HHPC3).  Typically, this can only be done once a day and only in few specific areas.  One can see that, such manufacturing environment can not react to contamination quick enough which result in defects and fallouts. 

“A Better Turnkey Solution for Particle Monitoring!”

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.  A Bluetooth Low Power (BLE) Mesh network is used to transmit data from the sensors back to a local backend server as shown in the Figure-2 below.  Real-time data are collected with the options to either be analyzed locally or through a cloud network.

Figure-2 MtM+’s Mesh PSN Solution

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.  The size of the PSN box is 75mm by 72mm by 42mm (see below figure), which is small enough to be effectively placed in most environments. 

“Every labor counts! Time really matters!”

Utilizing the Mesh PSN technology can not only help reduce the labor cost and time, but also provide a much better and efficient way to help you analyze and collect the data precisely and further improve the quality of the manufacturing.  Figure-3 is a comparison between manual measurement and measurement using mesh PSNs.

Figure-3 Comparison between MtM+ solution and manual measurement

“Actual Deployment at the Customer’s Cleanroom.”

On-site deployment is never an easy task. However, MtM+’s dedicated special solution team will find out the best locations for particle monitoring and quickly deploy our wireless mesh nodes without rewiring or opening a big hole and re-bury tubes or wires in the wall. The figure-4 below is a real case of our on-site PSN deployment at one of our semiconductor customer’s factory floor. 8 cleanroom monitoring segments have been pre-defined by our customer and we decided to place our 15 pcs of PSNs at those selected critical area after our inspection and discussion with customer. Only one server is needed to collect those scattered sensor nodes due to the advantage of MtM+’s Mesh PSN technology.

Figure-4 Customer Factory Floor Layout & Deployment

As one can see in the figures-5 below, our solution will help visualize the data collected to help user better monitor the fluctuation of particle changes (one frame per 10mins, 144 frames per day) more frequently at the whole area within the whole day, comparing to manually measuring approach at much fewer spots and mostly 2~3 times per day in the past.

Our customer actually got notified by more alarms and thus captured an issue on the manufacturing floor, and later resolve the problem that could potentially harm it’s process by replacing the floor pieces with ventilated ones.

Figure-5 Left: 24-hours Space distribution of particle numbers.
Right: 24-hours Accumulative alarm events counts by clean room segments

The figure-6 below are the standard features of our server-based dashboard GUI that can further help users perform device management and data analysis etc.

Figure-6 MtM+ Smart Factory Dashboard Features

“More Applications on The Way”

Beside measuring air quality in a clean room, due to the small size of these PSN nodes, it can reside inside the manufacturing equipment itself (see figure-7 below).  This takes particle measurement to another level and ensuring maxim µm yield in any manufacturing environment.

Figure-7 Particle Sensing Nodes Reside Inside Main Equipment

“We need your Feedback!”

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, and we will cover more about our calibration process and benchmark details in the next article, Smart Factory: Particle Monitoring PartII.

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

Smart Factory: Big Data and Cloud Solutions

In any smart factory systems, huge amount of real-time data are generated.  In order to efficiently and effectively analyze these data, cloud computing must be used over traditional local computing methods.  MtM+’s smart factory solution is cloud ready and this article aim to discuss this in detail.  The following diagram illustrates one particular implementation on a motion detection system.

MtM’s DCS Data Collection Station collects real-time motion (vibration) data from all the machines in operation.  This data is sent to a local server through BLE MESH network.  Traditionally, the data will be stored and analyzed locally.  In the cloud ready solution, the local server doesn’t analyze this data but instead transmit all the real time data to a cloud server over the internet.  The storage and analyze are done via a vast cloud server network.

One might ask, why go through the internet when his can be handled locally.  The following tables look at the pros and cons of both solutions.

For a smart factory, the amount of data generated is enormous.  It requires tremendous processing power to analyze this locally which requires significant investment in initial setup and continual maintenance.  Many companies see the benefits of shared computing power and have setup cloud systems to perform preciously that.  They charge a monthly or yearly fee for their cloud service which includes everything from data download to data analysis.  This is definitely a more cost effective solution for most companies.

The single most concerning issue with a cloud computing solution is security.  Any system that is online is prone to attacks from hackers which can be catastrophic.  A local system is definitely the most secure as everything is kept locally but one must weigh between cost and security.   MtM+’s current cloud solution is setup with the Alibaba Cloud (Aliyuan).  This is highly secure and is outlined in details in the following link:    The following diagram illustrates preciously MtM+’s implementation.

The vibration sensors detect real-time data where the Data Collection Stations (DCS) transmit this information to local server/gateway through either Bluetooth Low Power (BLE) or LoRa interfaces.  Once the gateway successfully collect these data on the local server, the on-premise Dashboard will display these in detail before uploading them onto the cloud server (in this case, Aliyuan is used).  Once uploaded, the information can be accessed and analyzed anywhere with an internet connection.   The following figure shows two different dashboards, the left being the local server and the right being the cloud server.

Looking at our sample case of vibration detection, the local (on-premise) server can generate status reports and display real time data. This data is also coarsely analyzed to detect abnormal activities (ie power outages). While these features are available for both local and cloud servers, local server do offer some additional services. An on-premise SQL database on the local server acts as a backup to the generated data. If needed, the DCS devices can be reprogrammed or modified locally but not through the cloud.

The cloud servers however offer very powerful computing power which would be very expensive to implement for any company. With the cloud, these big data can be analyzed effectively and efficiently with any combination of parameter matrices as shown in the following diagram.

Provided the advanced security of most available cloud service providers today, the benefits of their advance analytical tools make cloud computing a vital part of any smart factory implementation. For any question and inquiry, please email MtM+ at

Smart Factory: Thermal Image Recognition Systems

The latest development in MtM+’s Smart Factory solution is the incorporation of Thermal Image Recognition into the existing Data Collection Station (DCS).   The main targets of this new technology are factories that store their chemicals in tanks.  Whether the chemical is hazardous or not, the storage of these substances must be taken seriously.

Heat, is a particularly important matrix in terms of storing condition.  Certain chemicals when heated up will release harmful by-products that are a risk to health.  Unanticipated reaction of certain chemical might release a lot of heat and may result in explosions.  This calls for improved real-time monitor systems that can control water spray systems or inform an evacuation when necessary.  The following figure provides an overview of such systems.

Data Collection Station (DCS) is equipped with various wireless connectivity options and various I/O ports for smart factory implementation.  See Specifications here (    A DCS implemented with an IR camera generates real-time image data and transmit such data to the Cloud or the Local WAN through a LoRa network.   LoRa is a digital wireless network that is capable for long range transmissions up to 10 km in rural areas.  The DCS’s LoRa system is implemented using MtM+’s M908 module (see specification here –   With this data securely on the Cloud or the Local WAN, it can be accessed by a Personal Computer (PC) through a Wifi Access Point (AP) for further analysis.   With these data available on the PC, reactionary systems can be implemented based on the incoming data.

The IR camera selected for this project is the Radiometric Lepton 2.5 manufactured by FLIR Systems.   It features a small size of 8.5×11.7×5.6mm, 80hx60v pixel resolution, 9Hz frame rate with I2C/SPI interfaces.  The most important feature of the Lepton 2.5 is the radiometric capability compared to its predecessors.  Lepton 2.5 can output a temperature value for each pixel in a frame irrespective of the camera temperature with an accuracy of +/-5˚C.

With the 14-bit pixel temperature value (in Kelvin) successfully collected into the PC, they are divided into different blocks for analysis, see image below.

This system has been implemented and tested with a Petrochemical company to provide a real-time monitor system for the safety of their chemical storage (see images below).

Press Release: MtM+ Technology partnered with leaders in the semiconductor industry launching their smart factory solution.

[Taipei, Taiwan, Nov 6th] MtM+ Technology has partnered with leaders in the semiconductor industry launching their first smart factory solution.  Many months of research with MtM+’s strategic partners identified many areas where smart technology can be effectively applied.  Customized DCS machines were designed and deployed at several factories where real-time valuable data were collected and analyzed.

The first area identified was semiconductor wafer storage.  In order to avoid oxidation, a constant flow of nitrogen is pumped into these storage cabinets to maintain constant temperature and humidity.  These cabinets were not constantly monitored and controlled leading to a high risk of wafer spoilage.  MtM+ installed custom DCS machines to monitor temperature and humidity and applied nitrogen accordingly.  This not only provided savings in nitrogen but implemented a disaster preventive monitoring system which is extremely valuable.

Electricity is one vital component in operating any factory.  Power outages cause downtime for manufacturing, resulting in heavy monetary losses.  The second area identified is power outage due to the overheating of supply circuit breakers.  When these breakers overheat, they will be triggered providing a temporary loss of power.  The factory’s existing solution to this problem is to have a worker to measure this with a thermometer gun and adjust the machine loading accordingly.  This is an extremely manual process and can fail often in the hot summer months.  MtM+ installed custom DCS machines to all circuit breakers at several factory locations to monitor its temperature.  Warning messages will be sent out to modify machine loading if it is above a certain threshold.  Since such preventive monitor system has been installed, no power outages related to overheating has been identified.

Over many months, these two systems were deployed and extremely valuable real-time data had been collected.  Nitrogen cost is cut by 40% and labor cost is cut by 45%.  While cost cutting is great, the most important are deployment of these real-time preventive monitor systems.  These avoided chances of wafer spoilage as well as power outages caused by power breakers overheating.

As the data generated by these real-time preventive monitor systems accumulate to big data, it provided great opportunity for analysis through algorithms with artificial intelligence (AI).  With the available technology, machine learning is possible providing deeper insights and predictions to future disaster prevention.  Installation of smart factory systems like MtM+’s DCS machines is only the beginning to the gateway of big data AI analysis.

If you would like more information about this topic, please email for details.

Press Release: MtM+ Technology launches Data Collection Station, a complete Smart Factory Solution.

[Taipei, Taiwan, Oct 3rd] Smart factory is the inevitable future of manufacturing.  MtM+ Technology is releasing two new products in the Smart Factory sector: Data Collection Station, DCSv1 and DCSv2.   Both products feature Bluetooth Low Power (BLE) technology (with options of Wifi and LoRa) armed with various sensors and controls tailored to the needs of each factory.

With MtM+’s Smart Factory system, it effectively provides a solution for Environmental Monitoring, Preventive Maintenance and Asset Management.  Valuable real-time data can be collected and analyzed for the purpose of both cost reduction and production down-time prevention.  Several machines that need to be serviced can be identified through Smart Factory systems and can be taken out at the same time ensuring smooth manufacturing operation.

As the number of DCS stations grows, a bottleneck is created in terms of data transmission causing huge data corruption.  Through research, it’s found that data collision result in a data loss of as much as 80%.  In order to recover this data, MtM+ has done extensive research and development in Edge Computer technology.   Smart algorithms are developed and tested to battle this issue.  This solution has been tested thoroughly and currently deployed in several companies in the Semiconductor, Petrochemical and CNC Manufacturing industries.

The deployment of these Smart Factory machines in these factories are proven valuable and a cost reduction of 76-83% has been identified.  Beside cost savings, the data collected provided an unseen insight to these operations in the areas of logistics, resource allocation and effective time management.  DCSv1 and DCSv2 are incredibly flexible, reliable and cost effective machines for any large scale smart factory solution.

If you would like more information about this topic, please email for details.

Enabling Smart Factory with Edge Computing

Smart Factory is the future in manufacturing.  Integration of smart technologies such as IoT, AI and machine learning in factory equipment allows for real time analysis and immediate reactive actions that can maximize output and minimize downtime.  In this article, we will explore what is Edge Computing and its importance to a smooth operation in a Smart Factory.

What is Bluetooth Mesh Networking?

With many sensors and IoT devices installed in the many factory equipment, these devices communicate with each other and the main server through a Bluetooth network.  Such a network is also referred to as a Bluetooth Mesh.  The size of this mesh can be tens, hundreds or even thousands of nodes.  More details of how such Bluetooth Mesh can be found here:

What is Edge Computing?

On the wiki, Edge Computing is defined as, “In one vision of this architecture, specifically for IOT devices, data comes in from the physical world via various sensors, and actions are taken to change physical state via various forms of output and actuators; by performing analytics and knowledge generation at the edge, communications bandwidth between systems under control and the central data center is reduced.”, so what does this mean?

It is best to illustrate edge computing via an example.  There are five temperature sensors setup around the house to monitor temperature change and each of these sensors sends its current temperature to the main server directly.  Without edge computing, if there are no changes in the temperature, all five sensors send the same temperature reading continuously to the main server.  As you can see, this is a waste of energy and causes collision (we will discuss this in subsequent sections).  With edge computing, an algorithm can be developed on each of the sensors.

It is designed so that each sensor won’t sent temperature information to the main server unless it’s has a change of at least 0.5 degrees.  This way, the sensors will only send “useful” data and won’t update main server data unless there is a big enough change.  This is the main philosophy of edge computing.

MtM+ Technology’s Edge Computing Research and Solution

MtM+ Technology has implemented Smart Factory solutions through its Data Collection Stations (DCS) (Details and specification outlined here:  DCS collects environmental data and communicates with other DCS units and the main server through a Bluetooth Mesh network.

MtM+ Technology partnered with a company in the Semiconductor Industry to implement a smart cabinet for silicon wafer storage.  Silicon wafers can oxide (rust) quickly with oxygen therefore must be stored in nitrogen cabinets.  Maintaining a proper level of nitrogen in these cabinets is proven a challenge and traditionally, nitrogen is just refilled regularly over time.  It is found in our research that the level of nitrogen in these cabinets has a strong relationship with temperature and humidity.  DCS machines are equipped with temperature and humidity sensors and will replenish nitrogen accordingly to changes in these two matrices.  Many units of these DCS machines were implemented in the many nitrogen cabinets and worked really well.  As more units were installed, problems arose.   Please see the following chart.

It is found that as the number of DCS machines increased, the percentage of data loss to the main server increased exponentially.  For example, if there are only five people talking to you at once, you are very likely to hear fully what they are talking to about.  Suddenly if there are twenty people talking to you at once, some of the voices will surely collide and you will miss some of the information.  This data collision  is precisely the problem and is the main cause of this data loss.  This data loss resulted in inaccurate temperature and humidity readings which interfered with the smart control of nitrogen replenishment to the wafer cabinets.

Looking at this issue, edge computing can fully resolve this.  An algorithm has been developed and implemented on each of the DCS units so that only “useful” information will be sent to the main server.  Referring to the chart below.

The black line represents humidity data measured in one DCS unit.  The red line shows humidity data received at the main server without edge computing while the blue line shows the same data with edge computing in place.   As can be seen in this plot, with edge computing, a very accurate representation of the real data is achieved (Pearson’s R of 0.9906).

Why Bluetooth and Edge Computing over Wifi?

Smart factory sensors and the communication interface with the main server can also be implemented using a Wifi network.  Unlike Bluetooth, a Wifi network is not limited by bandwidth so edge computing is not needed.  MtM+ Technology has done extensive research comparing the two technologies and found that Bluetooth implementation result in an average cost savings of 76-83% over Wifi.   Here are the findings.

Interface with the main server

Each Wifi sensor node connects to the main server through a wireless router (called Access Point or AP).  Each of these routers can connect up to 30 sensor nodes.   If there are 250 sensors, 9 routers will be needed.  For Bluetooth mesh connection to the main server, only one dongle is needed for up to 500 sensor nodes.  As the number of sensors in the network increase beyond 30, the need for addition routers for Wifi implementation increases cost dramatically.  This is before comparing the cost of a Wifi router over a Bluetooth dongle which is easily 8 times.

Network Infrastructure and physical size of sensor network

Each Wifi router in a Wifi network requires a physical wire (or fiber optics) to be connected to the main server and/or the internet.  A wireless repeater can be used but with each repeater, the network bandwidth will be cut in half.  As bandwidth decreases, it faces with issues like data loss.  A Bluetooth mesh sensor network, however, do not require a physical wire network.  As long as each Bluetooth sensor node is within 30 meters of each other, data can be transmitted to the main server through each of the nodes and the dongle.  For older factories, the cost to implement large scale physical wired networks will be extremely expensive.  Without many obstructions, each Wifi router has network coverage up to 300 squared meters.  For large factories, a large number of Wifi routers will be needed which makes it less feasible when compared to a Bluetooth mesh network.


Smart factory is the inevitable future of manufacturing.  Illustrated in our research, as the number of DCS units increased, the data loss to the main servers can be as much as 80%.  With the implementation of edge computing algorithms, very accurate representation of real data can be achieved.  The advantages of a Bluetooth mesh network over Wifi were also discussed and it was found that a cost savings of 76-83% was achieved.  It is concluded that a Bluetooth mesh network along with edge computing is proven to be a vital and cost effective part of any large scale smart factory solution.