面向極大規(guī)模集成電路封裝X射線檢測的圖像處理關(guān)鍵問題研究
發(fā)布時(shí)間：2018-11-12 14:19
【摘要】：極大規(guī)模集成電路產(chǎn)業(yè)是國民經(jīng)濟(jì)和國防建設(shè)的重要支柱之一,是衡量一個(gè)國家和地區(qū)科技實(shí)力的重要標(biāo)志。隨著集成電路多功能、高密度、集成化多層封裝的發(fā)展,傳統(tǒng)的自動光學(xué)檢測只能對表面缺陷進(jìn)行檢測,難以實(shí)現(xiàn)集成化多層封裝的內(nèi)部缺陷檢測。X射線通過透射成像可直觀地檢測出元器件的內(nèi)部缺陷,為極大規(guī)模集成電路產(chǎn)業(yè)提供了非常有效的檢測手段。然而,集成電路多由高密度材質(zhì)組成且結(jié)構(gòu)細(xì)小,形成的X射線圖像具有信噪比低、對比度低、有效特征細(xì)小等特點(diǎn),難以實(shí)現(xiàn)有效的檢測。內(nèi)部缺陷檢測精度直接影響集成電路產(chǎn)業(yè)的封裝質(zhì)量,因此,研究面向集成電路封裝X射線檢測的圖像復(fù)原方法具有理論意義和很高的應(yīng)用價(jià)值。本文面向極大規(guī)模集成電路X射線檢測研究了正則化框架下的圖像復(fù)原問題,重點(diǎn)研究了適用于稀疏性約束的2 1l-l模型和適用于噪聲去除的全變分(Total Variation,TV)正則化模型下的快速性算法以及在物理運(yùn)動角度和導(dǎo)數(shù)空間上的新模型建立。主要研究工作如下:1)研究了一種基于動量梯度下降方向的快速去模糊算法,解決了常規(guī)梯度下降方向在處理2 1l-l模型下的圖像復(fù)原問題時(shí)收斂速度慢、對噪聲極為敏感的問題。該算法通過模擬物理學(xué)中的動量性質(zhì),引入了動量梯度下降方向作為迭代方向,使得算法在平滑區(qū)域增大步長,提高算法收斂速度;在噪聲區(qū)域利用當(dāng)前迭代的動量特性阻止下降速度的突然改變,避免算法收斂至局部最優(yōu)解。本文在理論上給出并證明了算法穩(wěn)定解存在的充要條件。標(biāo)準(zhǔn)灰度圖像和集成電路X射線圖像兩個(gè)系列的實(shí)驗(yàn)證明了算法的快速性和有效性。2)通過近點(diǎn)理論推導(dǎo)得到了一類正則化項(xiàng)不可微的復(fù)原模型在動量梯度下降方向下的凸二階近似模型,結(jié)合全變分正則化項(xiàng)提出了基于動量梯度投影的TV正則化圖像復(fù)原算法并在理論上證明了其收斂性,避免了TV正則項(xiàng)的不可微性帶來的計(jì)算困難。最后,實(shí)驗(yàn)結(jié)果證明了該算法在計(jì)算速度上的優(yōu)越性以及在去噪和細(xì)節(jié)保持方面的有效性。3)從物理運(yùn)動角度出發(fā),將算法迭代過程看作粒子的移動過程,在2 1l-l模型基礎(chǔ)上提出了基于物理總能量目標(biāo)函數(shù)的稀疏重建模型及算法,并對算法的收斂性進(jìn)行了證明,解決了傳統(tǒng)方法中模型非單調(diào)非強(qiáng)凸且建模及求解過程均未考慮物理演化規(guī)律的問題。其中,粒子的運(yùn)動模型是在粘性介質(zhì)、牛頓流體中以迭代結(jié)果為位移、迭代次數(shù)為離散時(shí)間、2 1l-l模型為重力勢能函數(shù)建立的運(yùn)動模型。與多種算法的對比實(shí)驗(yàn)證明了本文方法速度快、重建質(zhì)量高等優(yōu)點(diǎn),表明了該方法更適合于實(shí)際的X射線缺陷檢測。4)利用圖像空間和導(dǎo)數(shù)空間的關(guān)聯(lián)性,在導(dǎo)數(shù)空間中建立了基于梯度分離的各向異性TV和各項(xiàng)同性TV正則化圖像復(fù)原模型,并采用Split Bregman框架提出了相應(yīng)的各向異性算法和各向同性算法,解決了傳統(tǒng)模型保真項(xiàng)未考慮導(dǎo)數(shù)空間具有提高圖像復(fù)原成功率這一優(yōu)勢的問題。該方法在導(dǎo)數(shù)空間內(nèi)實(shí)現(xiàn)了水平梯度和垂直梯度的解耦,有利于圖像去噪和細(xì)節(jié)保持能力的提升。實(shí)驗(yàn)結(jié)果表明,相較于傳統(tǒng)圖像空間中的TV正則化方法,該方法具有更優(yōu)的去噪能力和細(xì)節(jié)保持特性。
[Abstract]:The very large-scale integrated circuit industry is one of the important pillars of national economy and national defense construction, and is an important sign to measure the strength of science and technology in a country and region. With the development of the multi-function, high-density and integrated multi-layer package of the integrated circuit, the traditional automatic optical detection can only detect the surface defects, and it is difficult to realize the internal defect detection of the integrated multi-layer package. The X-ray can directly detect the internal defect of the component through the transmission imaging, and provides a very effective means for detecting the large-scale integrated circuit industry. however, that integrate circuit is composed of high-density material and the structure is small, the formed X-ray image has the characteristics of low signal-to-noise ratio, low contrast, small effective characteristic and the like, and is difficult to realize effective detection. The precision of internal defect detection directly affects the packaging quality of the integrated circuit industry. Therefore, the research of the image restoration method facing the integrated circuit package X-ray detection is of great theoretical significance and high application value. In this paper, the problem of image restoration under the regularized framework is studied for the X-ray detection of a very large-scale integrated circuit. The 2l-l model and the total variation for noise removal are studied. The fast algorithm under the regularization model of TV and the establishment of a new model on the physical motion angle and the derivative space. The main research work is as follows: 1) A fast de-fuzzy algorithm based on momentum gradient descent direction is studied, which solves the problem that the convergence speed is slow and the noise is extremely sensitive when the image restoration problem of the conventional gradient descent direction under the processing of the 2l-l model is solved. The method introduces the momentum gradient descent direction as the iteration direction by simulating the momentum property in physics, so that the algorithm increases the step size in the smooth region, and the convergence speed of the algorithm is improved; and the sudden change of the falling speed is prevented by the momentum characteristic of the current iteration in the noise region, and the algorithm is avoided to converge to the local optimal solution. In this paper, the necessary and sufficient conditions for the existence of the stable solution of the algorithm are given in this paper. The experiments of two series of standard gray-scale image and integrated circuit Xray image show the rapidity and validity of the algorithm. 2) The convex second-order approximate model of a kind of regularized non-differentiable recovery model in the direction of momentum gradient descent is derived by the near-point theory. In this paper, a TV regularization image restoration algorithm based on momentum gradient projection is proposed in combination with all-variable regularization term, and its convergence is proved theoretically, and the computational difficulty of the non-differentiability of the TV regular term is avoided. In the end, the experimental results show the superiority of the algorithm in the calculation speed and the validity of the de-noising and detail maintenance. 3) From the point of physical motion, the iterative process of the algorithm is regarded as the moving process of the particles. On the basis of the 2l-l model, a sparse reconstruction model and an algorithm based on the physical total energy objective function are proposed, and the convergence of the algorithm is proved. The motion model of the particle is in the viscous medium, the iteration result is the displacement in the Newtonian fluid, the number of iterations is the discrete time, and the 2l-l model is the motion model established by the gravity potential energy function. The comparison experiments with a variety of algorithms have proved that the method has the advantages of high speed, high reconstruction quality and the like, and shows that the method is more suitable for practical Xray defect detection. 4) The relevance of the image space and the derivative space is utilized, In the derivative space, an anisotropic TV based on gradient separation and an image restoration model of the same-sex TV are established, and a corresponding anisotropic algorithm and an isotropic algorithm are proposed by using the Split Bregman framework. The problem that the traditional model fidelity term does not take into account the derivative space has the advantage of improving the image restoration success rate is solved. The method realizes the decoupling of the horizontal gradient and the vertical gradient in the derivative space, and is beneficial to the improvement of the image de-noising and the detail maintenance capability. The experimental results show that the method has better de-noising capability and detail-preserving characteristics than the TV regularization method in the traditional image space.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP391.41
本文編號：2327357
[Abstract]:The very large-scale integrated circuit industry is one of the important pillars of national economy and national defense construction, and is an important sign to measure the strength of science and technology in a country and region. With the development of the multi-function, high-density and integrated multi-layer package of the integrated circuit, the traditional automatic optical detection can only detect the surface defects, and it is difficult to realize the internal defect detection of the integrated multi-layer package. The X-ray can directly detect the internal defect of the component through the transmission imaging, and provides a very effective means for detecting the large-scale integrated circuit industry. however, that integrate circuit is composed of high-density material and the structure is small, the formed X-ray image has the characteristics of low signal-to-noise ratio, low contrast, small effective characteristic and the like, and is difficult to realize effective detection. The precision of internal defect detection directly affects the packaging quality of the integrated circuit industry. Therefore, the research of the image restoration method facing the integrated circuit package X-ray detection is of great theoretical significance and high application value. In this paper, the problem of image restoration under the regularized framework is studied for the X-ray detection of a very large-scale integrated circuit. The 2l-l model and the total variation for noise removal are studied. The fast algorithm under the regularization model of TV and the establishment of a new model on the physical motion angle and the derivative space. The main research work is as follows: 1) A fast de-fuzzy algorithm based on momentum gradient descent direction is studied, which solves the problem that the convergence speed is slow and the noise is extremely sensitive when the image restoration problem of the conventional gradient descent direction under the processing of the 2l-l model is solved. The method introduces the momentum gradient descent direction as the iteration direction by simulating the momentum property in physics, so that the algorithm increases the step size in the smooth region, and the convergence speed of the algorithm is improved; and the sudden change of the falling speed is prevented by the momentum characteristic of the current iteration in the noise region, and the algorithm is avoided to converge to the local optimal solution. In this paper, the necessary and sufficient conditions for the existence of the stable solution of the algorithm are given in this paper. The experiments of two series of standard gray-scale image and integrated circuit Xray image show the rapidity and validity of the algorithm. 2) The convex second-order approximate model of a kind of regularized non-differentiable recovery model in the direction of momentum gradient descent is derived by the near-point theory. In this paper, a TV regularization image restoration algorithm based on momentum gradient projection is proposed in combination with all-variable regularization term, and its convergence is proved theoretically, and the computational difficulty of the non-differentiability of the TV regular term is avoided. In the end, the experimental results show the superiority of the algorithm in the calculation speed and the validity of the de-noising and detail maintenance. 3) From the point of physical motion, the iterative process of the algorithm is regarded as the moving process of the particles. On the basis of the 2l-l model, a sparse reconstruction model and an algorithm based on the physical total energy objective function are proposed, and the convergence of the algorithm is proved. The motion model of the particle is in the viscous medium, the iteration result is the displacement in the Newtonian fluid, the number of iterations is the discrete time, and the 2l-l model is the motion model established by the gravity potential energy function. The comparison experiments with a variety of algorithms have proved that the method has the advantages of high speed, high reconstruction quality and the like, and shows that the method is more suitable for practical Xray defect detection. 4) The relevance of the image space and the derivative space is utilized, In the derivative space, an anisotropic TV based on gradient separation and an image restoration model of the same-sex TV are established, and a corresponding anisotropic algorithm and an isotropic algorithm are proposed by using the Split Bregman framework. The problem that the traditional model fidelity term does not take into account the derivative space has the advantage of improving the image restoration success rate is solved. The method realizes the decoupling of the horizontal gradient and the vertical gradient in the derivative space, and is beneficial to the improvement of the image de-noising and the detail maintenance capability. The experimental results show that the method has better de-noising capability and detail-preserving characteristics than the TV regularization method in the traditional image space.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP391.41
【參考文獻(xiàn)】
相關(guān)期刊論文 前2條
1 石光明;劉丹華;高大化;劉哲;林杰;王良君;;壓縮感知理論及其研究進(jìn)展[J];電子學(xué)報(bào);2009年05期
2 汪雪林,韓華,彭思龍;基于小波域局部高斯模型的圖像復(fù)原[J];軟件學(xué)報(bào);2004年03期
相關(guān)博士學(xué)位論文 前3條
1 喬田田;基于ι_1優(yōu)化模型和Bregman迭代的圖像恢復(fù)算法研究[D];哈爾濱工業(yè)大學(xué);2014年
2 易麗婭;圖像復(fù)原的Bregman迭代正則化方法研究[D];華中科技大學(xué);2011年
3 豐國棟;數(shù)字化X線攝影圖像增強(qiáng)方法研究[D];中國科學(xué)技術(shù)大學(xué);2009年
相關(guān)碩士學(xué)位論文 前3條
1 徐寒;面向集成電路封裝檢測的X射線圖像濾波與增強(qiáng)方法研究[D];華南理工大學(xué);2013年
2 褚夫國;面向元器件封裝過程的X光圖像增強(qiáng)分割及測量技術(shù)[D];華南理工大學(xué);2012年
3 杜琨;基于X射線圖像印刷電路板缺陷檢測和標(biāo)記技術(shù)[D];中北大學(xué);2009年
，本文編號：2327357
本文鏈接：http://sikaile.net/shoufeilunwen/xxkjbs/2327357.html
最近更新
教材專著
