本文選題：集成電路芯片 + 缺陷檢測(cè)��； 參考：《東南大學(xué)》2016年博士論文

【摘要】：集成電路芯片已廣泛應(yīng)用于多個(gè)領(lǐng)域,但是制造過(guò)程中產(chǎn)生的缺陷會(huì)直接影響集成電路芯片的壽命和可靠性。傳統(tǒng)的人工檢測(cè)方法,存在耗時(shí)長(zhǎng),勞動(dòng)強(qiáng)度大,誤檢率高等缺點(diǎn),已無(wú)法適應(yīng)生產(chǎn)的需求。機(jī)器視覺(jué)檢測(cè)技術(shù)通過(guò)機(jī)器視覺(jué)的方法對(duì)產(chǎn)品進(jìn)行分析處理,檢驗(yàn)產(chǎn)品是否符合質(zhì)量要求,對(duì)保障產(chǎn)品質(zhì)量,提高產(chǎn)品合格率起到了關(guān)鍵作用。本文結(jié)合國(guó)家自然科學(xué)基金資助項(xiàng)目(50805023)和江蘇省科技成果轉(zhuǎn)化專項(xiàng)(BA2010093),以集成電路芯片表面缺陷為研究對(duì)象,展開視覺(jué)檢測(cè)關(guān)鍵技術(shù)研究,所從事的主要研究工作如下:(1)集成電路芯片表面缺陷圖像多閾值分割針對(duì)集成電路芯片表面缺陷圖像的特點(diǎn),提出基于螢火蟲算法的二維熵多閾值缺陷圖像分割法。首先,將二維熵閾值分割擴(kuò)展為二維熵多閾值分割。其次,分析螢火蟲算法仿生原理和尋優(yōu)過(guò)程。最后,將二維熵作為螢火蟲算法的目標(biāo)函數(shù),對(duì)多閾值尋優(yōu)。實(shí)驗(yàn)結(jié)果表明,基于螢火蟲算法的二維熵多閾值缺陷圖像分割法能有效分割集成電路芯片表面缺陷;運(yùn)行速度較窮舉法有很大的提高;在閾值選取的準(zhǔn)確性、計(jì)算時(shí)間和峰值信噪比方面均優(yōu)于基于粒子群算法的二維熵多閾值分割法;但是仍然存在實(shí)時(shí)性不足的問(wèn)題。針對(duì)集成電路芯片表面缺陷圖像多閾值分割計(jì)算量大、計(jì)算時(shí)間長(zhǎng)的問(wèn)題,提出基于反向螢火蟲算法的大津多閾值缺陷圖像分割法。首先,將大津閾值分割擴(kuò)展為大津多閾值分割。其次,提出一種反向螢火蟲算法。該算法將反向?qū)W習(xí)算法中反向解可能比當(dāng)前解距離目標(biāo)函數(shù)更近的思想引入螢火蟲算法,增加螢火蟲的多樣性和全局搜索能力。最后,將最大類間方差作為反向螢火蟲算法的目標(biāo)函數(shù),對(duì)多閾值尋優(yōu)。實(shí)驗(yàn)結(jié)果表明,基于反向螢火蟲算法的大津多閾值缺陷圖像分割法的性能優(yōu)于窮舉法、基于粒子群算法、螢火蟲算法的大津多閾值分割法:但是該分割法在四閾值分割時(shí)出現(xiàn)了一些尋優(yōu)結(jié)果不準(zhǔn)確的現(xiàn)象。為了分割集成電路芯片表面缺陷圖像,提出基于改進(jìn)的螢火蟲算法的大津多閾值缺陷圖像分割法。針對(duì)螢火蟲算法全局搜索和局部搜索不平衡的現(xiàn)象,提出改進(jìn)的螢火蟲算法。在該算法中,提出基于Cauchy變異的多樣性增強(qiáng)策略和鄰域策略,并根據(jù)不同的停滯狀態(tài),選擇不同的策略以增強(qiáng)全局搜索能力并提高收斂性能。將改進(jìn)的螢火蟲算法應(yīng)用于大津多閾值分割中,對(duì)閾值尋優(yōu)。實(shí)驗(yàn)結(jié)果表明,基于改進(jìn)的螢火蟲算法的大津多閾值缺陷圖像分割法不僅能有效分割缺陷圖像,并在準(zhǔn)確性、計(jì)算時(shí)間、收斂性和穩(wěn)定性方面整體優(yōu)于基于達(dá)爾文粒子群算法、混合的差分進(jìn)化算法、螢火蟲算法、反向螢火蟲算法的大津多閾值分割法。(2)集成電路芯片表面缺陷提取集成電路芯片表面缺陷明場(chǎng)圖像中存在噪聲干擾缺陷的提取。為了提取明場(chǎng)圖像中的缺陷,提出基于數(shù)學(xué)形態(tài)學(xué)變換和改進(jìn)的區(qū)域生長(zhǎng)的缺陷提取法。首先,根據(jù)圖像灰度級(jí),獲得明場(chǎng)圖像四閾值分割后的淺層缺陷圖像和深層缺陷圖像。其次,針對(duì)淺層缺陷圖像和深層缺陷圖像的不同特點(diǎn),將不同的數(shù)學(xué)形態(tài)學(xué)操作應(yīng)用于兩種圖像以去除噪聲點(diǎn),定位缺陷所在的區(qū)域。最后,提出改進(jìn)的區(qū)域生長(zhǎng)法來(lái)提取明場(chǎng)圖像中的缺陷。實(shí)驗(yàn)結(jié)果表明,該方法能有效提取明場(chǎng)圖像中的缺陷。集成電路芯片表面缺陷暗場(chǎng)圖像中存在芯片表面紋理干擾缺陷的提取。為了提取暗場(chǎng)圖像中的缺陷,提出基于紋理方向檢測(cè)和缺陷區(qū)域選擇的缺陷提取法。首先,針對(duì)暗場(chǎng)圖像的特點(diǎn),提出芯片表面紋理方向檢測(cè)算法。其次,根據(jù)圖像灰度級(jí),獲得暗場(chǎng)圖像四閾值分割后的暗缺陷圖像和明缺陷圖像。最后,針對(duì)暗缺陷圖像和明缺陷圖像的不同特點(diǎn),以缺陷紋理方向與芯片表面紋理方向不一致為原則,提出兩種不同的缺陷區(qū)域選擇的方法以提取暗場(chǎng)缺陷。實(shí)驗(yàn)結(jié)果表明,該方法能有效提取暗場(chǎng)圖像中的缺陷。(3)集成電路芯片表面缺陷特征提取與降維為了提取集成電路芯片表面缺陷特征,分別從幾何特征、紋理特征和灰度特征三個(gè)方面提取32個(gè)特征。提取的幾何特征包括面積、周長(zhǎng)、緊湊性、重心坐標(biāo)、矩形度、占空比、偏心率和Hu不變矩。提取的紋理特征為14個(gè)灰度共生矩陣特征。提取的灰度特征包括灰度均值、灰度方差和灰度熵。由于特征維數(shù)較多,采用主成分分析特征抽取法和基于KNN的序列浮動(dòng)前向特征選擇法分別進(jìn)行特征降維。主成分分析法根據(jù)特征值累積貢獻(xiàn)率的取值大于90%的原則,將32維特征降至6維�；贙NN的序列浮動(dòng)前向特征選擇法將每個(gè)特征的KNN分類性能作為序列浮動(dòng)前向選擇的評(píng)價(jià)函.數(shù)以實(shí)現(xiàn)特征選擇,將32維特征降至10維。(4)集成電路芯片表面缺陷分類識(shí)別為了識(shí)別并分類集成電路芯片表面缺陷,分析討論了 BP神經(jīng)網(wǎng)絡(luò)和RBF神經(jīng)網(wǎng)絡(luò)。為了提高支持向量機(jī)的分類識(shí)別率,提出基于改進(jìn)的螢火蟲算法的支持向量機(jī),其基本思想是將分類識(shí)別率作為目標(biāo)函數(shù),通過(guò)改進(jìn)的螢火蟲算法對(duì)支持向量機(jī)中的懲罰參數(shù)和核函數(shù)參數(shù)進(jìn)行尋優(yōu)。將8種芯片缺陷對(duì)應(yīng)的主成分分析法抽取的6維特征和基于KNN的序列浮動(dòng)前向特征選擇法選擇的10維特征分別輸入三個(gè)分類器,形成六組分類器。實(shí)驗(yàn)結(jié)果表明,特征選擇法選擇的特征作為基于改進(jìn)的螢火蟲算法的支持向量機(jī)的輸入時(shí),分類性能高于其他五組分類器,識(shí)別率為91.367%。本文對(duì)缺陷多閾值分割、缺陷提取、缺陷特征提取與降維、缺陷分類識(shí)別等視覺(jué)檢測(cè)關(guān)鍵技術(shù)進(jìn)行研究,在理論研究和技術(shù)研發(fā)等方面取得了一定的成果,為集成電路芯片表面缺陷視覺(jué)檢測(cè)提供了理論指導(dǎo)和技術(shù)支撐。
[Abstract]:Integrated circuit chips have been widely used in many fields, but the defects produced in the manufacturing process will directly affect the life and reliability of integrated circuit chips. The traditional artificial detection methods have the disadvantages of long time consuming, high labor intensity and high false detection rate, which have been unable to meet the needs of production. Machine vision detection technology is used by machine vision. Methods to analyze and deal with the product, test whether the product meets the quality requirements, and play a key role in ensuring the quality of the product and improving the rate of product qualification. This paper combines the National Natural Science Fund Project (50805023) and the Jiangsu province science and technology achievement transformation special (BA2010093), and the integrated circuit chip surface defect as the research object. The main research of the key technology of visual detection is as follows: (1) a two-dimensional entropy multi threshold defect image segmentation method based on the firefly algorithm is proposed. First, the two-dimensional entropy threshold segmentation is extended to a two-dimensional entropy multithreshold. Secondly, the bionic principle and optimization process of the firefly algorithm are analyzed. Finally, the two-dimensional entropy is used as the target function of the firefly algorithm to optimize the multi threshold. The experimental results show that the two-dimensional entropy multi threshold defect image segmentation method based on the firefly algorithm can effectively segment the surface defects of the integrated circuit chip, and the running speed is more than the poor method. Large improvement, the accuracy of threshold selection, calculation time and peak signal to noise ratio are superior to two-dimensional entropy multi threshold segmentation based on particle swarm optimization, but there is still a problem of lack of real time. First, the Otsu threshold segmentation is extended to the multi threshold segmentation of the Otsu threshold. Secondly, a reverse firefly algorithm is proposed. The algorithm introduces the idea that the reverse solution in the reverse learning algorithm may be more close to the target function than the current solution to the firefly algorithm to increase the diversity of the firefly. In the end, the maximum inter class variance is used as the target function of the reverse firefly algorithm to optimize the multi threshold. The experimental results show that the performance of the multi threshold image segmentation method based on the reverse firefly algorithm is superior to the exhaustive method, based on the particle swarm optimization and the multi threshold segmentation method of the firefly algorithm, but the segmentation method is used. In order to divide the image of the surface defect of the integrated circuit chip, a new method of multi threshold defect image segmentation based on improved firefly algorithm is proposed in order to divide the surface defect image of the integrated circuit chip. The improved firefly algorithm is proposed for the global search and local search imbalance of the firefly algorithm. In the algorithm, the diversity enhancement strategy and neighborhood strategy based on Cauchy mutation are proposed, and different strategies are selected to enhance the global search ability and improve the convergence performance according to the different stagnation states. The improved firefly algorithm is applied to the Otsu multi threshold segmentation and the threshold optimization. The experimental results show that the improved firefly calculation is based on the improved firefly calculation. The method not only can effectively segment the defect image, but also is better than the Darwin particle swarm optimization, the hybrid differential evolution algorithm, the firefly algorithm, the reverse firefly algorithm and the Otsu multi threshold segmentation method. (2) the surface deficiency of the integrated circuit chip. In order to extract the defects in the image of the bright field, a defect extraction method based on the mathematical morphology transformation and the improved region growth is proposed. First, the shallow defect image and the deep defect image after the four threshold segmentation of the field image are obtained according to the gray level of the image. Secondly, in view of the different characteristics of the shallow defect image and the deep defect image, different mathematical morphology operations are applied to two images to remove the noise point and locate the region where the defect is located. Finally, an improved region growth method is proposed to extract the defect in the bright field image. The experimental results show that the method can effectively extract the bright field. The defect in the image of the integrated circuit chip is extracted from the defect in the surface texture of the chip. In order to extract the defects in the dark field image, a defect extraction method based on the texture direction detection and the defect region selection is proposed. Firstly, the texture direction detection algorithm of the chip surface is proposed for the characteristics of the dark field image. At the same time, the dark defect image and the bright defect image after the four threshold segmentation are obtained according to the gray level of the image. Finally, according to the different characteristics of the dark defect image and the bright defect image, two different defect region selection methods are proposed to extract the dark field defect with the principle that the defect texture direction is inconsistent with the texture direction of the chip surface. The experimental results show that the method can effectively extract the defects in the dark field image. (3) the feature extraction and dimension reduction of the surface defects of the IC chip are extracted and reduced to extract the features of the surface defects of the integrated circuit chip, and 32 features are extracted from the geometric features, the texture features and the gray features respectively. The extraction geometry features include the area, the circumference, and the compact features. Sex, center of gravity, rectangles, duty ratio, eccentricity, and Hu invariant moments. The extracted texture features are 14 gray level co-occurrence matrix features. The extracted gray level features include gray mean, gray variance and gray entropy. The feature extraction method and KNN based sequence floating forward feature selection method are used respectively because of the large number of feature dimensions. The principle component analysis is based on the principle that the value of the cumulative contribution rate of the eigenvalue is greater than 90%, and reduces the 32 Vitter sign to 6 dimension. The KNN classification performance of each feature is used as the evaluation function of the sequence floating forward selection based on the sequence floating forward feature selection method based on KNN. The feature selection is realized and the 32 Vitter sign is reduced to 10 dimension. (4) integration In order to identify and classify the surface defects of IC chips, the BP neural network and RBF neural network are analyzed and classified. In order to improve the classification recognition rate of support vector machines, a support vector machine based on improved firefly algorithm is proposed. The basic thought is to use the classification recognition rate as the target function. The improved firefly algorithm optimized the penalty parameters and kernel parameters in the support vector machine. The 6 dimension feature extracted by the principal component analysis and the 10 dimension feature selection based on the KNN based sequence floating forward feature selection method, respectively, input three classifiers to form six groups of classifiers. The experimental results show that the characteristics of the 8 kinds of chip defects are characterized. The feature of selection method selected as the input of support vector machine based on improved firefly algorithm, the classification performance is higher than the other five groups of classifier. The recognition rate is 91.367%.. This paper studies the key technology of defect multi threshold segmentation, defect extraction, defect feature extraction and dimensionality reduction, defect classification recognition and so on. Some achievements have been achieved in technology research and development, providing theoretical guidance and technical support for visual inspection of integrated circuit chip surface defects.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN407;TP391.41

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