#### Topic provider

In the past 40 years, the Electronic and Optoelectronic System Research Laboratories undertakes the missions for the researches and developments of Semiconductor, Packaging, LED/OLED/Micro LED, Telecommunications, Flexible Electronics, 3D Imaging, Flexible Display and Transparent Display Application System technologies.

ITRI provides a welcoming work environment that stimulates the full potential of ITRI’s members, allowing them to excel and to perform their best in application-oriented researches. We fully cultivate the capability of independent innovation through participating in international cooperation and academic programs. These collaborative activities are pursued so as to aid in the success of technological incubation and entrepreneur start-ups. We strive to strengthen the technological innovation and value creation ability for Taiwan’s industry in order to remain the competitiveness globally.

#### Introduction

Automated optical inspection (AOI) [1] is an automated visual inspection of printed circuit board (PCB) (or LCD,transistor) manufacture where a camera autonomously scans the device under test for both catastrophic failure (e.g. missing component) and quality defects (e.g. fillet size or shape or component skew). It is commonly used in the manufacturing process because it is a non-contact test method. It is implemented at many stages through the manufacturing process including bare board inspection, solder paste inspection (SPI), pre-reflow and post-reflow as well as other stages. The Institute of Electronics and Optoelectronics in Industrial Technology Research Institute(ITRI) has spent years on developing flexible displays, hoping to elevate the production quality with AOI technology during the pilot run. This time we have invited experts from different fields to join us, and focus on identifying defect classifications of AOI image data that are offered so as to elevate the identification efficiency of AOI through statistical science.

This is an open-ended problem that can be solved consistently over a long period of time, and has offered milestone rewards for participants. Baseline is the best score of the previous milestone, and Private Leaderboard will be updated occasionally.

Reference
[1] https://en.wikipedia.org/wiki/Automated_optical_inspection

#### Activity time

The milestone is set at 23:59:59 on 09/30/2019, with its Private Leaderboard being announced at 00:00:00 on 10/01/2019. The grade would be viewed as a basis for awards. Private Leaderboard will be updated occasionally before the milestone.

During the period from 6/6 to 9/30, Private Leaderboard will be updated once two weeks.

#### Prize Information

The final score should be uploaded before 23:59:59 on the day of milestone. The top three exceeding Baseline score (0.998521) on Private Leaderboard would be awarded as follows:

The first place: 100,000 discount points of hicloud

The second place: 100,000 discount points of hicloud

The third place: 100,000 discount points of hicloud

You will be awarded a certificate if your score outperforms Baseline on the Private Leaderboard and you pass model verification process

Rewards were provided by Chunghwa Telecom

Note:

1. Hicloud points can redeem service charge. After the discount, the price will be charged at 30% off based on the list price automatically.

2. Chunghwa Telecom deserves the right to make changes to the terms and conditions herein.

3. A model verification process will be performed to verify eligibility of awardees. Only those who pass model verification process will be awarded a certificate and/or prize.

#### Evaluation Criteria

After classification models of defect prediction are offered by researchers participating in the issue, the back-end of the system would process them in bathces regularly to calculate the score. Evaluations are conducted by calculating the corresponding accuracy rate of the actual value.

The following is the formula:

$$Accuracy = {\text{Number of correct predictions} \over \text{Number of total predictions}}$$

#### Data description

There are six categories included in image data that the issue offers (1 normal category + 5 defect categories)

• train_images.zip: image data for training (PNG format), 2,528 images in total.
• train.csv: includes 2 columns, ID and Label.
• ID: the image filename
• Label: defect classification category (0: normal, 1: void, 2: horizontal defect, 3: vertical defect, 4: edge defect, 5: particle)
• test_images.zip: image data for testing (PNG format), 10,142 images in total.
• test.csv: includes 2 columns, ID and Label.
• ID: the image filename
• Label: defect classification category (the value can be only one of the following numbers: 0, 1, 2, 3, 4, 5)

Please upload the file in CSV format, separate with commas, and save in one file. The content must correspond to number order of ID columns and include the following information.

• ID: the image filename
• Label: defect classification category (the value can be only one of the following numbers: 0, 1, 2, 3, 4, 5)

#### Rules

• Upload limit is 5 entries per day.
• The evaluation result would be based on the final version that you upload. If two teams (or more) got the same evaluation scores, the time they upload would determine the ranking.
• If there is cheating or fraud during the activity, the team that cheats will be disqualified from the activity and the vacancy would be filled up by other teams in the ranking order.
• There is no team-up option for this activity. Please inform AIdea administrator first if you are a team.
• After uploading, the answers of test data would be divided into two parts to calculate the score:
• Before the deadline of the activity: The system refers to partial Ground Truth of test data to examine and calculate the score. The result will be posted on Public Leaderboard as reference for the final score and ranking. This data accounts for 40% of the entire test data.
• After the deadline of the activity: The system refers to Ground Truth of the remaining test data to examine and calculate the score. The result will be posted on Public Leaderboard as reference for the final score and ranking.
• Any form of manual labeling is forbidden. A model verification process will be performed to verify eligibility of awardees. Only those who pass model verification process will be awarded a certificate and/or prize.

0

1floor

(活動到9月底為止?)

0

3floor

###### eric2018/08/24 16:11
0

4floor

@tcglarry 您好，我是這個議題的負責人 Eric。目前議題的活動時間已延長至 9/27，歡迎您繼續參與本議題並上傳結果！

0

5floor

###### eric2018/08/31 08:37
0

6floor

@stephanie_huang 您好，謝謝您對於「AOI瑕疵分類」議題的關注與參與。

0

7floor

0

8floor

0

9floor

###### tcglarry2018/09/03 22:29
0

10floor

a.只分類瑕疵類別， 對實際生產有幫助嗎 ？
b. 正確率多少以上， 模型會有佈建的效益 ?

0

11floor

0

12floor

###### eric2018/09/21 13:16
0

13floor

@tcglarry 您好，分類瑕疵類別，有助於判斷所生產的產品是否有問題，並針對不同類別做進一步的分析；至於正確率，廠商在實際評估效益時，會依照不同的產品及應用有不同的要求，謝謝！

0

14floor

###### eric2018/10/09 13:57
0

15floor

@franky 您好，AOI 瑕疵分類議題活動已圓滿結束，結果在 10/04 已透過快訊公佈。我們目前也在籌畫社群相關功能，以供大家交流，敬請期待，謝謝！

0

16floor

###### eric2018/10/31 11:29
0

17floor

taurus12079@gmail.com 您好，AOI議題將規劃以不同的形式持續解題，所以暫時不會將test的 label公開，敬請見諒，謝謝！

###### JC2018/11/08 17:30
0

18floor

@eric 這個dataset還有可能供練習使用嗎?

###### eric2018/11/27 11:13
0

19floor

@JC，您好，未來會推出不同形式的解題方式，敬請期待！

###### aiedward2019/04/04 22:27
0

20floor

aoi.zip 無法下載，經常中斷

###### eric2019/04/08 09:34
0

21floor

@aiedward，您好，經測試目前可以正常下載，請再嘗試下載，謝謝！

Ranking Team name Grade of Result
1 blessxu 0.9992601
2 cwhuang1021 0.9982737
4 sln95303 0.9970406
5 fhwang 0.9967940
6 Colmar 0.9965474
7 tw48964896 0.9953144
8 jesse1029 0.9950678
9 LiuPiu 0.9945745
10 stephan.kuo 0.9945745
11 SnorLax 0.9938347
12 JackKao 0.9935881
13 Jiang 0.9935881
14 jummy1124@gmail.com 0.9935881
15 gn00665219 0.9933415
16 ridicon 0.9930949
17 tueswking511 0.9928483
18 hsh0703 0.9928483
19 pacude881 0.9918618
20 107378057 0.9918618
21 supertortoise 0.9918618
22 linan 0.9911220
23 Rsys 0.9911220
24 lucas85062055 0.9908754
25 inture 0.9906288
26 A610050A 0.9903822
27 painting 0.9898890
28 johnny88850tw 0.9896424
29 chihyuu 0.9893958
30 slot9004 0.9893958
31 ab30310 0.9886559
32 franky 0.9884093
33 benq581 0.9876695
34 andysu840520 0.9874229
35 Colin 0.9871763
36 itri456133 0.9869297
37 kevin32111@gmail.com 0.9859432
38 ERR1121 0.9854500
39 p692584k 0.9854500
40 chaoziyin 0.9837237
41 jamychen1126 0.9832305
42 yonglee 0.9829839
43 panzer 0.9824907
44 paul369xd 0.9819975
45 thanhphat1221 0.9815043
46 VanSuHuynh 0.9812577
47 Allen 0.9797780
48 cheney235689 0.9797780
49 tommy860823 0.9797780
50 pups2468 0.9795314
51 mingj 0.9792848
52 taurus12079 0.9787916
53 Lucas7246 0.9782983
54 heysun0728 0.9780517
55 ee255852 0.9775585
56 ian06013101799 0.9775585
57 lunaseaman 0.9773119
58 flovebrs 0.9768187
59 igormorawski 0.9763255
60 chtte13 0.9760789
61 TerrenceChou 0.9758323
62 ChenJH 0.9755856
63 RayChang 0.9736128
64 tcglarry 0.9718865
65 larry 0.9704069
66 y987141 0.9699136
67 katychou 0.9689272
68 YCLo 0.9689272
69 chris880622 0.9681874
70 gash8234 0.9667077
71 redsunmirage 0.9654747
72 105021044 0.9652281
73 RogerHung 0.9639950
74 amberliu1985 0.9635018
75 socrateschch 0.9605425
76 yomingjayism 0.9600493
77 jackevin 0.9590628
78 hsin8824 0.9580764
80 build0220 0.9543773
81 t107598068 0.9543773
82 joker 0.9442663
83 geniuspingping 0.9442663
84 franco0088 0.9422934
85 ag150300 0.9408138
86 Sandra 0.9400739
87 yuanyuanjerry 0.9383477
88 jack8566662 0.9353884
89 imlm0001 0.9324290
90 chouchienu 0.9324290
91 small77 0.9119605
92 ping27942 0.9067817
93 gene3706 0.8939580
94 b10556007 0.8604192
95 ainglee 0.8579531
96 siyu026019 0.8453760
97 ccccu33333 0.8268803
98 cianyu150cjo 0.8256473
99 w10041656 0.7972872
100 gcpyobcmst001 0.7819975
101 be3575 0.6848335
102 wayne9458 0.6281134
103 DuyDinh 0.4623921
104 pek9527 0.4545006
105 B10456005 0.3447595
106 taurus12079@gmail.com 0.3282367
107 or624 0.2663378
108 Mingyenchu 0.2660912
109 timsimtho 0.2140567
110 PDFwithData 0.2029593