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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

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