Vision based wafer states detection in front opening unified pod load-port system

MATEC Web of Conferences 336, 02029 (2021) CSCNS2020 https://doi.org/10.1051/matecconf/202133602029 Vision based wafer states detection in front opening unified pod load-port system Qiang Zhang1, *, Xueying Sun1, and Mingmin Liu2 1School of Electronic Information, Jiangsu University of Science and Technology, No. 666 Changhui Road, Zhenjiang, 212003 China 2SIASUN Robot & Automation CO., Ltd, No.33 Quanyun Road, Shenyang 110169, China Abstract. In modern integrated circuit manufacturing processes, wafers are always transported from one procedure to another. To reduce the risk of dust, Front Opening Unified Pod (FOUP) load-port system is always adopted. Misplaced wafers should be detected before transported. Traditional methods always fail to detect wafer states correctly. To improve detection accuracy, this paper proposed a vision based method. Wafer overlap and malposition detection approach based on modified YOLO-V3 algorithm was suggested. Experiment results shows superiority of the proposed approach. 1 Introduction In integrated circuit manufacturing, silicon chips are produced through multiple production processes. Wafer handling system plays a very important role in the factory for transporting wafers from one work flow to another. FOUP load-port system, which can be seen in Figure 1, is an equipment for recovering or placing silicon wafers from the processing unit through wafer box. In the FOUP load-port system, vacuum robot is used as loading and unloading mechanism. Fig. 1. FOUP load-port system. In some cases, wafers in the FOUP have the possibility of overlap or malposition. This would cause subsequent operations to fail. Overlap and malposition states can be seen in * Corresponding author: © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/). MATEC Web of Conferences 336, 02029 (2021) CSCNS2020 https://doi.org/10.1051/matecconf/202133602029 Figure 2. In order to correct these mistakes, wafer overlap or malposition detection procedures should take effect. However, traditional detection methods, for example opposite-type photoelectric switch based methods, cannot meet the requirements of highprecision detection. As a result, FOUP load-port system is hardly competent in the integrated circuit processing. (a) (b) (c) (d) Fig. 2. Silicon wafer states: (a)FOUP and wafers; (b)correct; (c)overlap; (d)malposition. In our work, we designed a vision based detection system to improve wafer overlap and malposition detection accuracy in FOUP load-port applications. Firstly, we choose monocular camera as the sensor. In order to improve system’s robustness, we carefully adjusted the position and the angle of view of the camera. Secondly, we proposed a wafer overlap and malposition detection approach based on improved YOLO-V3 algorithm. According to experiment results, the proposed method can provide high detection accuracy and efficiency. The rest of the paper is organized as follows. Section 2 introduces methods for object detection. Section 3 discussed details of the proposed approach for silicon wafer detection. Section 4 describes performance of the detection system and analyses experiment results. The last part, Section 5, gives the conclusion of the paper. 2 Related works In recent few years, some effective methods have been proposed to detect objects in images. Traditional detection methods like keypoint feature matching based methods[1–3], shape matching based methods[4,5], Hough transform based methods[6], machine learning based methods[7,8] were developed to recognize or detect objects in images. From year 2014, deep learning based methods were developed rapidly. Many deep learning architectures like Fast R-CNN[9], SSD[10], YOLO[11] have been developed to revolutionize object detection accuracy. As a regression based algorithm, YOLO and the subsequent improved algorithm get more and more attentions. This paper introduced a method inspired by YOLO-V3[12] architecture, and the approach was successfully applied in wafer states detection. 3 Robust wafer overlap and malposition detection approach Original images have lots of background information. These background information has no contributions to the detection results. To improve detection robustness, only regions containing wafers are reserved. Then, projective transformation and downsampling are performed to obtain 416×416 size images. And the results are feed into our designed detection neural network. 2 MATEC Web of Conferences 336, 02029 (2021) CSCNS2020 https://doi.org/10.1051/matecconf/202133602029 Input image (size: 416,416,32) Conv 32×3×3+ Conv 64×3×3_s2 (Output size: 208,208,64) Residual Block 1×64 (Output size: 208,208,64) Conv 128×3×3_s2 (Output size: 104,104,128) Residual Block 2×128 (Output size: 104,104,128) Conv 8×1×1 (Output size: 26,26,8) Residual Block 8×512 (Output size: 26,26,512) Conv 512×3×3_s2 (Output size: 26,26,512) Residual Block 8×256 (Output size: 52,52,256) Conv 256×3×3_s2 (Output size: 52,52,256) Fig. 3. The proposed neural network The designed approach is inspired by YOLO-V3 architecture. The difference between YOLO-V3 and ours are reflected in two aspects. The first is that we simplified the original YOLO-V3 network. The second is that the output results are truncated to meet the detection needs. The structure of the proposed neural network can be seen in Figure 3. In the wafer states detection system, we divide waters’ states into three classes: correct, overlap and malposition. To address the detection problem, each detection result is represented as a 26×26×8-dimentional tensor. The result indicates that there are 26×26 detection result and each result is a 8-dimentional vector, which can be written as {tx, ty, tw, th, p, Ccorrect, Coverlap, Cmalposition}. In the 8-dimentional vector, {tx, ty, tw, th} indicates the predicted bounding box. We first give a reference bounding box to help the network to obtain accurate detection results. Reference bounding box is chosen by clustering labeled data. For the reason that the size of the reflective edges of wafers are similar, only one reference bounding box is chosen. In the detection result, tx and ty are offsets between predicted center and reference bonding box center. tw and th are width and height scale ratios between predicted box and reference bounding box. p is the confidence score of the predicted bounding box. Ccorrect, Coverlap and Cmalposition are correct, overlap and malposition class probabilities. 4 Experiment and results analysis 4.1 Methodology In our experiment, PyTorch open source library was employed and CUDA computation acceleration toolkit was introduced to training and deploy procedures. The software and hardware configurations can be seen in Table 1. Table 1. Hardware configuration in the experiment. Hardware CPU Computer Memory Size (...truncated)