［ Electronic Equipment ］ Information Equipment

Edge AI Unit, MR100Keywords

Artiﬁcial Intelligence (AI), Sensing data, Predictive maintenance

Abstract02

Makoto Hojo,
Yuka Oishi,
Yasuyuki Iwauchi

Since the 1990s, we have been supplying industrial controllers to semiconductor manufacturing companies. Due to recent technological developmentsin the ﬁeld of Internet of Things (IoT) and Artiﬁcial Intelligence (AI), manufacturing industries call for the introduction of “predictive maintenance.”
Under predictive maintenance, the service provider monitors the machineries or facilities during normal operations; if there are potential warning signs(any symptom of fault or failure), it will inspect, repair, or replace, faulty parts.
Unlike the “preventive maintenance” that conducts scheduled maintenance(inspecting, repairing, or replacing parts and components at a predeterminedpoints), predictive maintenance can make efﬁcient replacements of parts andcomponents.
For the purpose of predictive maintenance, we developed the Edge AI Unit,
MR100.

1 Preface

2 Outline of the MR100

To perform “predictive maintenance” to semiconductor manufacturing equipment and variousautomatic machines, we developed the Edge
Artiﬁcial Intelligence (AI) Unit, MR100. We installed
the RZ/T1 Series embedded processor by Renesas
Electronics Corporation. The MR100 is a device
using Edge AI (AI algorithms on a hardware device)that collects sensor data from equipment with asensor device which detects the equipment anomaly based on the collected data.
A feature of the MR100 is that it can be retroﬁtted to equipment for measuring and it can decidelocally based on the machine learning results without connecting to the external networks such as thecloud. In the semiconductor industry, they demandthat repair and inspection service providers help
prolong the operational life of used semiconductor
manufacturing equipment. For this reason, this unit
is appropriate for predictive maintenance. In thesemiconductor industry, sending device production
information to external networks like the cloud isprohibited. The MR100 can start AI learning oncethe customer prepares a PC running Windows OS
and connects it to the MR100. We can expect many
similar applications in the semiconductor industry.

2.1 Independent Decision Making Based onAI Learning

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Fig. 1 shows connections between the MR100and equipment. In order for the MR100 to performan AI-based decision, it is necessary to “learn” thespeciﬁc unique feature of sensor data from that
equipment through the setup process. Since there
are many algorithms/techniques needed for AI
learning, the MR100 uses an “auto-encoder” unsupervised learning technique for Neural Networks(NNs) and the auto-encoder mechanically detects
Installed a RZ Series
processor – “e-AI” techfor factory automation

Equipment

Independent decisionby AI (normal/abnormal)

Sensor datacollection

Sensor datacollection

Sensing data
monitoring

Writing ofthe resultof learning

Edge AI Unit

Drafting AI
parameters

Collected
dataAI learning
serverOut of our scopeof supply

Fig. 1Connections between the MR100 and Equipment

The relationship in connections between the MR100 and equipment is shown.

Edge AI unit

AI learning server
Learning

Data
input

Input
layer

Collected
data

OutputHidden layer
layer

Input
signalNetwork output

NNs configuration data

Fig. 2

Learned NNs
parameter

NNs parameterand NNs
configuration
data are loaded.

Parameter is determined so that theoutput value assumes the same resultof the input value. → Learning
NNs configuration data

Learned NNs
parameter

Determined NNs
parameter bymachine learning

Method of AI Learning

The method of AI learning is shown for an Edge AI unit and AI learning server.

and learns the regularity in the collected sensor
data and then applies the learned results into theanomaly detection.
Currently, the semiconductor manufacturingindustry anomaly detection of equipment is madethrough a system called the Fault Detection andClassiﬁcation (FDC) system. The FDC is a statistical analysis on sensor data from the equipment.
Since professional knowledge like statistical analysis is needed for the FDC, predictive maintenancefor equipment faces the issues of different levels of
anomaly detection due to different skill levels ofeach analyst. Meanwhile, predictive maintenanceby the MR100 is made by AI-based decision making, and as such, the data analyst does not require
such professional knowledge as a statistical analysis. Fig. 2 shows the method of AI learning. The
data collected by the MR100 is transferred to the AI
learning server where the AI learning parameters
are established based on the data gathered with ahelp of the AI learning program. Fig. 3 shows the AI
learning parameters. The AI decision is made possible by simple loading the sensor data onto theMR100. After the data loading operation, the MR100
can be operated locally without connecting to the AI
learning server. The MR100 identiﬁes the deviation
between the AI learned sound data waveform andthe just uploaded sensor data waveform; if the deviation level exceeds the pre-determined values(deviation value deemed to be anomaly), the MR100
will detect and determine it as an anomaly. Such a
speciﬁed value is set up according to the level ofexpertise of the user by drawing on the experiencesof the facility management. The AI learning program

Edge AI unit

User server

Hierarchical network
Input
Outputlayer Hidden layer
layer

Data
input

Input
signalExecution of NNs
by inference
according to theNNs parameter andNNs data structure

Fig. 3(Independent decision)
Notified onlyin the caseof anomaly

Networkoutput

AI Learning Parameters

This shows a concept of AI learning parameters which aredrafted on the data collected by using the AI learning program.

is also made by the user by using the machinelearning framework (libraries), like “TensorFlow” or“Caffe.”

2.2 Data Processing by the Edge Computing
Since the MR100 is installed with μITRON-areal-time OS for embedded systems, it can collectsensor data at much higher speed than othergeneral-purpose OSes. Fig. 4 shows the difference
in data collection. By virtue of high-speed sensor
data collection, it is possible to capture previously
omitted waveforms due to the slow speed of theconventional model. This model can detect ananomaly in more highly accurate manner. Since theMR100 can locally detect an anomaly in the sensor
data by AI decision, it can upload the result to theupstream IT System only when such anomaly isdetected. Thanks to this edge computing, the network load can be reduced in comparison with a
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Measurement data

Measurement data

1s samplingTime(a) Conventional data collection

10ms samplingLAN

Abnormal waveform?

Collection of SECS
(HSMS) messages

Switching Hub(with mirror ring function)

Analoginput

Time(b) High-speed data collection

AI tool/SCADAEquipment

Fig. 4

Difference in Data Collection

A difference is shown between former data collection and highspeed data collection by the MR100. Thanks to the high-speed
data collection, it is now possible to capture previously omitted
waveforms.

Application ofSolvent A

Application ofSolvent B

Communications Network in Semiconductor
Manufacturing Plant

2.3 Conformity to SEMI Equipment Communications Standard (SECS)

4000(min－1) 3000
2000
1000
012

3

4

56(step)

789101178910117891011(a) Recipe 1

5000
4000(min－1) 3000
2000
1000
0123456(step)(b) Recipe 25000
4000(min－1) 3000
2000
10000Fig. 6(SECS tool)Spin dry

50000Acquisition of learningparameter ID for theapplicable formula

Communications network and positioning of the MR100 in semiconductor manufacturing factory are shown.

[Graph Legend]

0MES HOST

01

23456(step)

Fig. 5 shows the difference in semiconductor
manufacturing equipment. The semiconductor manufacturing equipment performs different controls
according to the type of a product being manufactured, even if the same process chamber ※1 is used.
Since the control patterns (such as quantity andtiming) are different every time, multiple learningparameters are needed that have learned the sensor data for the execution of each particular formula※2. These multiple learning parameters must be
used properly according to the characteristic of theexecution formula. Fig. 6 shows a communications
network in a semiconductor manufacturing plant.
While the MR100 monitors the SECS communication messages between semiconductor manufacturing equipment and the MES HOST, the MR100
decides the execution recipe at the given processchamber in the semiconductor manufacturing
equipment, and automatically switches the learningparameters applicable to the formula.

(c) Recipe 3Fig. 5

Difference in Semiconductor Manufacturing
Equipment

The difference in semiconductor manufacturing equipment isshown with sample patterns.

previous system performing the AI decision at anupstream IT system. Since the networks are connected with various IT devices and communication
units, the heavy network load can substantiallyaffect the production volume.

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2.4 Method of Management
Before use, various settings are required for
the MR100 in order to determine where and how
the MR100 collects the sensor data and where
the result of the decision on the collected data isalerted. In the case of the MR100, communicationinterface speciﬁcations are disclosed to our productcustomers and setup software is prepared/procuredby the customers. Fig. 7 shows an image of MR100
setup software and Fig. 8 shows that of the data
notiﬁcations. By setting up various operating conditions in advance, it is also possible to notify of theTable 1 MR100 Specifications
A list of MR100 Speciﬁcations is shown.
Item

RZ/T1
Main CPU: ARM
Cortex-R4F (600MHz)NOR Flash Memory128MB (512Mbit × 2)

SDRAM

128MB (512Mbit × 2)

EEPROM

16kB

SPI Flash64MB

Image of MR100 Setup Software

An image of MR100 setup software is shown.

Fig. 8MPU

Image of Data Notifications

Data notiﬁcations for the MR100 in its normal state are shown.

anomaly detection only if it occurs, or if the occurrence of a fault together with data is during normal
operation.

3 MR100 Hardware (H/W)
Speciﬁcations
Table 1 shows the MR100 speciﬁcations. As
an input interface for the sensor data, an analog
input port is provided that consists of 8 channels for
single end inputs. The sampling speed is set up to50μs. If any anomaly is detected in the sensor data,
an error alarm signal is generated and transmitted
via the Ethernet or digital output port to external
equipment. A USB 2.0 interface is located in thebase section of this unit so that the sensor data can

Various interfaces

Fig. 7

Specifications

Analog Input (AI)8ch
(Single end: 8ch/Differential: 4ch)
※Input voltage: ±12V

Digital Input (DI)

4ch
※Rated input voltage:DC24V

Digital Output (DO)4ch
※Rated output voltage:
DC24V

Serial port (RS-232C)Transfer rate:115.2kbps (Max.)

2ch

LAN (100BASE-TX )3 ports

LED

Power source LED (Green),
general-purpose LED × 4,
LED for LAN (LINK/ACT,SPEED)DC 24V source

6pin

USB host (USB2.0)

1 portDIP switch

8bit × 1Dimensions

W140 × H200 × D47mm

Mass

Approx. 1kg

Working temperature

0〜45℃

Working humidity

30〜85％Rh (without dewcondensation)

Environment

Freedom from corrosive
gases and/or conductive
dust

Cooling system

Natural air cooling

Applicable standard

NilRoHS

RoHS-compliant

be saved by using a USB device. For better userinterface, major interfaces are put together on
the front panel of this unit. The dimensions andmass are W140 × H200 × D47mm (protruded partsexcluded) and 1kg respectively. It is an easy-tomanage size. For installation, a dedicated mounting
metal ﬁtting is provided for easy ﬁtting for the DIN
Rail Mounting.

4 Postscript
According to a trial calculation by a researchcompany in Japan, the AI markets for Japanesemanufacturing industry are estimated to become 1trillion yen in 2021. As such, we intend to offer a
one-stop service in order to support our customers
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from the early Edge AI Unit introduction study stage
unit to a stage where our products are being used
for their mass production operations. At the introduction study stage of our potential user, we willlease an MR100 starter kit that contains a MR100
unit, a set of acceleration sensor and trial version
software (a setup software and an AI learning program) so that the potential user can test drive if ourMR100 can ﬁt with their manufacturing equipment.
We supplied our past industrial controllers mostlyfor semiconductor manufacturing equipment. Wewill develop the sales of the MR100 mostly for semiconductor manufacturing equipment. By drawing on
our accumulated expertise, we will develop thesales of the MR100 into non-semiconductor manufacturing equipment like various automation equip-

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ment. We will work on improving the functions ofthe MR100.
・Windows is a registered trademark of Microsoft Corporation,U.S.
・TensorFlow is a registered trademark of Google Inc.
・e-AI is a registered trademark of Renesas Electronics, Inc.
・All product and company names mentioned in this paper are
the trademarks and/or service marks of their respective owners.
(Notes)※1．Chamber: A hermetically sealed vessel where chemical or
physical reactions are caused.
※2．Recipe: Inputting data to specify the processing method andparameters for the process at the manufacturing equipment.