Auto Encoders based Neural Networks to Predict Faultiness of VLSI Circuits

R. Gurunadha*, K. Babulu **
* Department of Electronics and Communication Engineering, JNTUK University College of Engineering, Vizianagaram, Andhra Pradesh, India.
** Department of Electronics and Communication Engineering, JNTUK University College of Engineering, Kakinada, Andhra Pradesh, India.
Periodicity:January - June'2020
DOI : https://doi.org/10.26634/jdp.8.1.17781

Abstract

The connections between circuit components in VLSI design are made by using a liquid crystal polymer substrate. Due to mistakes made by manufacturing machines and design engineers, there is a possibility of getting faulty connections in very large integrated circuits (VLSI). The faults in printed circuit boards (PCB) can be identified by using advanced technologies like computer vision and deep convolutional neural networks. In this paper, a model based on neural networks to detect faultiness of design has been proposed. The 3-4 Convolutional Neural Networks (CNN) model in deep learning can analyze the patterns in images and gives accurate results when the model is tested against new data. In our proposed model, an auto encoder based neural networks has been used for the detection of faults in printed circuit boards (PCB). An Artificial Intelligence (AI) camera to scan circuit design has also been used. The AI camera scan images at a higher resolution rate and at a higher dynamic range. The images taken by the camera are tested by the model to detect whether the image is faulty or not. The model is trained on a different set of circuit images and tested against validation data. Accuracy of 98.6% has been obtained by this approach.

Keywords

Keras, Image Processing, 3-4 CNN, Auto Encoders, PCB, AI Camera, Classification, VLSI Design.

How to Cite this Article?

Gurunadha, R., and Babulu, K. (2020). Auto Encoders based Neural Networks to Predict Faultiness of VLSI Circuits. i-manager's Journal on Digital Signal Processing, 8(1), 1-6. https://doi.org/10.26634/jdp.8.1.17781

References

[1]. Aakerberg, A., Nasrollahi, K., & Heder, T. (2017, November). Improving a deep learning based RGB-D object recognition model by ensemble learning. In 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) (pp. 1-6). IEEE.
[2]. Chan, T. H., Jia, K., Gao, S., Lu, J., Zeng, Z., & Ma, Y. (2015). PCANet: A simple deep learning baseline for image classification? IEEE Transactions on Image Processing, 24(12), 5017-5032. https://doi.org/10.1109/TIP.2015. 2475625
[3]. Costilla-Reyes, O., Scully, P., & Ozanyan, K. B. (2017). Deep neural networks for learning spatio-temporal features from tomography sensors. IEEE Transactions on Industrial Electronics, 65(1), 645-653. https://doi.org/10.1109/TIE.201 7.2716907
[4]. Ghamisi, P., Höfle, B., & Zhu, X. X. (2016). Hyperspectral and LiDAR data fusion using extinction profiles and deep convolutional neural network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(6), 3011-3024. https://doi.org/10.1109/JSTARS.2016.2 634863
[5]. He, M., & He, D. (2017). Deep learning based approach for bearing fault diagnosis. IEEE Transactions on Industry Applications, 53(3), 3057-3065.
[6]. Lee, J., Kim, T., Park, J., & Nam, J. (2017). Raw waveform-based audio classification using sample-level CNN architectures. In 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. Retrieved https://arxiv.org/pdf/1712.00866.pdf
[7]. Li, P., Chen, Z., Yang, L. T., Zhang, Q., & Deen, M. J. (2017). Deep convolutional computation model for feature learning on big data in internet of things. IEEE Transactions on Industrial Informatics, 14(2), 790-798.
[8]. Liu, B., Yu, X., Yu, A., & Wan, G. (2018). Deep convolutional recurrent neural network with transfer learning for hyperspectral image classification. Journal of Applied Remote Sensing, 12(2). https://doi.org/10.1117/1.JRS.12. 026028
[9]. Loghmani, M. R., Rovetta, S., & Venture, G. (2017, May). Emotional intelligence in robots: Recognizing human emotions from daily-life gestures. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1677- 1684). IEEE.
[10]. Luo, B., Wang, H., Liu, H., Li, B., & Peng, F. (2018). Early fault detection of machine tools based on deep learning and dynamic identification. IEEE Transactions on Industrial Electronics, 66(1), 509-518. https://doi.org/10.1109/TIE.2 018.2807414
[11]. Pan, J., Zi, Y., Chen, J., Zhou, Z., & Wang, B. (2017). Lifting Net: A novel deep learning network with layer wise feature learning from noisy mechanical data for fault classification. IEEE Transactions on Industrial Electronics, 65(6), 4973-4982. https://doi.org/10.1109/TIE.2017.276 7540
[12]. Shao, H., Jiang, H., Zhang, H., & Liang, T. (2017). Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network. IEEE Transactions on Industrial Electronics, 65(3), 2727-2736. https://doi.org/ 10.1109/TIE.2017.2745473
[13]. Shi, C., Panoutsos, G., Luo, B., Liu, H., Li, B., & Lin, X. (2018). Using multiple-feature-spaces-based deep learning for tool condition monitoring in ultra precision manufacturing. IEEE Transactions on Industrial Electronics, 66(5), 3794-3803. https://doi.org/10.1109/TIE.2018.28 56193
[14]. Sun, W., Zhao, R., Yan, R., Shao, S., & Chen, X. (2017). Convolutional discriminative feature learning for induction motor fault diagnosis. IEEE Transactions on Industrial Informatics, 13(3), 1350-1359.
[15]. Vafeiadis, A., Kalatzis, D., Votis, K., Giakoumis, D., Tzovaras, D., Chen, L., & Hamzaoui, R. (2017, November). Acoustic scene classification: From a hybrid classifier to deep learning. In Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE2017).
[16]. Wen, L., Li, X., Gao, L., & Zhang, Y. (2017). A new convolutional neural network-based data-driven fault diagnosis method. IEEE Transactions on Industrial Electronics, 65(7), 5990-5998. https://doi.org/10.11 09/TIE.2017.2774777
[17]. Yan, W., Tang, D., & Lin, Y. (2016). A data-driven soft sensor modeling method based on deep learning and its application. IEEE Transactions on Industrial Electronics, 64(5), 4237-4245. https://doi.org/10.1109/TIE.2016.26 22668
[18]. Yao, L., & Ge, Z. (2017). Deep learning of semi supervised process data with hierarchical extreme learning machine and soft sensor application. IEEE Transactions on Industrial Electronics, 65(2), 1490-1498. https://doi.org/10.1 109/TIE.2017.2733448
[19]. Zhao, M., Kang, M., Tang, B., & Pecht, M. (2017). Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes. IEEE Transactions on Industrial Electronics, 65(5), 4290-4300. https://doi.org/10.1109/TIE.2017.2762639
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