Abstract-Restoration of integrated circuit terahertz image based on wavelet denoising technique and the point spread function model

 

Zhirui Zhang, Yao Lua,  Cixing Lv, Qi Mao, Song long Wang, Shihan Yan, 

https://www.sciencedirect.com/science/article/abs/pii/S0143816620311878

In recent years, terahertz (THz) imaging technology has attracted much attention in the detection of the integrated circuit (IC). However, limited by the hardware of the imaging system, THz images often contain a significant amount of noise, which impairs the quality of the image details. The THz image is also degraded due to the long wavelength. In this study, we propose a novel method for THz image restoration. We first apply a wavelet denoising technique to process the THz time-frequency signal. The point spread function (PSF) is then mathematically modeled to restore the details of the IC image, as the degradation of the THz image can be regarded as the convolution process of the object equation and PSF. Finally, we compare the performance between the restored THz images before and after wavelet denoising. The results demonstrate that the restored image after denoising performs better in peak signal-to-noise ratio and visual improvements, proving the practicability and precision of our proposed method.

Abstract-Image fusion based on multiscale transform and sparse representation to enhance terahertz images

Qi Mao, Yunlong Zhu, Cixing Lv, Yao Lu, Xiaohui Yan, Dongshan Wei, Shihan Yan, and Jingbo Liu

Schematic diagram of the fusion method based on MST and SR.

https://www.osapublishing.org/oe/abstract.cfm?uri=oe-28-17-25293

High-quality terahertz (THz) images are vital to integrated circuit (IC) manufacturing. Due to the unique sensitivity of THz waves to different materials, the images obtained from the point-spread function (PSF) model have fewer image details and less texture information in some frequency bands. This paper presents an image fusion technique to enhance the resolution of THz IC images. The source images obtained from the PSF model are processed by a fusion method combining a multiscale transform (MST) and sparse representation (SR). The low-pass band is handled by sparse representation, and the high-pass band is fused by the conventional “max-absolute” rule. From both objective and visual perspectives, four popular multiscale transforms—the Laplacian pyramid, the ratio of low-pass pyramids, the dual-tree complex wavelet transform and the curvelet transform—are thoroughly compared at different decomposition levels ranging from one to four. This work demonstrates the feasibility of using image fusion to enhance the resolution of THz IC images.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Abstract-Convolutional neural network model based on terahertz imaging for integrated circuit defect detections

Qi Mao, Yunlong Zhu, Cixing Lv, Yao Lu, Xiaohui Yan, Shihan Yan, and Jingbo Liu

(a)Schematic of the T-Gauge 5000 system, (b) physical diagram of the THz-TDS system operating in the transmission mode.

https://www.osapublishing.org/oe/abstract.cfm?uri=oe-28-4-5000

Detection of integrated circuit (IC) defects is vital in IC manufacturing. Traditional defect detection methods have relied on scanning electron microscopy and X-ray imaging techniques that are time consuming and destructive. Hence, in this paper we considered terahertz imaging as a label-free and nondestructive alternative. This study aimed to use a convolutional neural network model (CNN) to improve the performance of the terahertz imaging IC detection system. First, we constructed a terahertz imaging IC dataset and analyzed it. Subsequently, a new CNN structure was proposed based on the VGG16 network. Finally, it was optimized based on its structure and dropout rate. The method proposed above can improve IC defects detection accuracy of THz imaging. Most significantly, this work will promote the application of terahertz imaging in practice and provide a foundation to further research in relevant fields.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement