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Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration
MR-11781; No of Pages 7 Microelectronics Reliability xxx (2015) xxx–xxx
Contents lists available at ScienceDirect
Microelectronics Reliability journal homepage: www.elsevier.com/locate/mr
Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration Guanglan Liao a, Pengfei Chen a,⁎, Li Du a, Lei Su a, Zhiping Liu b, Zirong Tang a, Tielin Shi a a b
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China School of Logistics Engineering, Wuhan University of Technology, Wuhan, Hubei 430063, China
a r t i c l e
i n f o
Article history: Received 17 May 2015 Received in revised form 1 September 2015 Accepted 10 September 2015 Available online xxxx Keywords: Three-dimensional integration Missing-bump defect X-ray Self-organizing map neural network
a b s t r a c t Three-dimensional integration has been a key technology in scientific research and industrial production of integrated circuits, where microbumps bridge multiple layers of chips. Microbump defect inspection, especially for missing-bump, is of major significance. We introduce self-organizing map network combined with X-ray imaging, and demonstrate a non-destructive method for rapid and effective inspection of missing microbump defects. 2D X-ray images of samples with microbumps are segmented, and vectors consisting of four features as representatives of microbumps are extracted. A self-organizing map network is constructed, and vectors of microbumps selected randomly from four samples are inputted into the network. Clusters of the defective bumps and the normal bumps are distinguished obviously. Then the other microbumps from the same samples are used for testing. The trained network can recognize the defective and normal microbumps through the clustering areas with no error. Microbumps from a different sample are inputted to the network for further verification, and high recognition accuracy is achieved. These prove the feasibility of using self-organizing map network for X-ray inspection of missing-bump defects. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction Electronic devices develop continuously towards smaller volume, lower power consumption, higher performance, more functions, and lower cost as well. Traditional electronic packaging technology has come across critical crises: the signal propagation delay increases significantly, and the feature dimension is pressing towards the physical limit, etc. [1,2]. Three-dimensional (3D) integration, which refers to the system integration technology that stacks and interconnects multilayer chips vertically, has now been generally considered as the most promising solution to this predicament. Combination of high aspect ratio through silicon vias (TSVs) and ultrafine pitch microbumps presents the shortest signal transmission channels between chips, making it the mainstream scheme of 3D integration [3–5]. Microbumps free from defects are of crucial concerns for high quality interconnects, since they are bridges between multiple chips [6]. However, the complexity of structures and difficulty in assembly processes make microbump defects arise easier during manufacturing. Effective and facilitated on-line diagnosis techniques for microbump defects, especially the missing-bump defects, are indispensable for realizing 3D integration with high reliability [7]. Non-contact defect inspection methods gain distinct advantages compared with contact methods since additional harms or even ⁎ Corresponding author. E-mail address: [email protected] (P. Chen).
destructions may happen to samples during contact detection procedures [8,9]. Optical microscopy measurement is the most common inspection technique in semiconductor industry, although it can only be used for surface examination of pre-bonded microbumps, as bonded bumps are hidden between chips which are usually optically opaque [10]. Infrared microscopy, with the ability of penetrating silicon material rather than metal materials, can be adopted for observation of specified processes, such as TSV profiles, wafer overlay alignments, etc. [11,12]. Owing to the strong capability of spreading through multiple materials, ultrasound inspection is of considerable importance to semiconductor quality control [13]. The most advanced scanning acoustic microscope (SAM) equipped with the gigahertz transducer can provide a resolution below 1 μm [14,15], although the necessity of coupling fluid brings lots of practical limitations. Nowadays, microfocus and nanofocus X-ray systems are widely used for various kinds of defects inspection in microelectronic packaging industry [16]. The latest X-ray inspection system has reached a spatial resolution better than 15 nm, and the computer tomography (CT) technique can provide detailed 3D features of detected samples. Undoubtedly, X-ray inspection, with the advantages of deep sensitivity, high stability, and wide applicability, is of great effectiveness for defect inspection in semiconductor industry [17,18]. In future 3D integration, the diameter of a microbump is expected to shrink down to 1 μm, and the density of microbumps will be up to 104/mm2 [19]. Ultra-massive microbumps make traditional detection systems that highly depend on inspectors' skills and constant mind
http://dx.doi.org/10.1016/j.microrel.2015.09.009 0026-2714/© 2015 Elsevier Ltd. All rights reserved.
Please cite this article as: G. Liao, et al., Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration, Microelectronics Reliability (2015), http://dx.doi.org/10.1016/j.microrel.2015.09.009
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G. Liao et al. / Microelectronics Reliability xxx (2015) xxx–xxx
concentration unsustainable, raising strong demands for automatic defect recognition and diagnosis techniques [20]. Machine vision, which imparts human intelligence to machines through adaptive learning approaches, has been widely used in manufacturing to evaluate the quality of products as a substitute for human visual inspection [21,22]. Artificial neural networks (ANNs), composed of collections of individual interconnected processing units, are particularly powerful in dealing with non-linear problems, and thus are commonly used for machine vision [23,24]. In this paper, the self-organizing map (SOM) neural network is applied for missing-bump defect inspection in 3D integration. The defects are artificially introduced during preparation of the samples. An X-ray inspection system is employed to capture images of the samples. The microbump areas are segmented based on canny operator and morphology operations. A random collection of microbumps containing normal microbumps and defective microbumps are inputted into the SOM network for training. The other microbumps from the same samples and those from a new sample are fed to the trained SOM network sequentially to verify the feasibility of the approach.
The minimum gray mini is defined by: mini ¼ faja≤b; a ∈ Gi ½n; b ∈ Gi ½ng
ð2Þ
The average gray μi is defined by: μi ¼
n 1X G ðkÞ n k¼1 i
ð3Þ
The standard deviation σi is defined by: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n u1 X 2 σi ¼ t ½G ðkÞ−μ i n k¼1 i
ð4Þ
Here Gi[n] is denoted as the set of gray values of the i-th microbump area consisting of n pixels, in which the gray value of the k-th pixel is denoted by Gi(k). These features are joined together to form a vector Fi which represents the i-th microbump:
2. Diagnosis approach F i ¼ ½maxi ; mini ; μ i ; σ i
ð5Þ
Three steps are carried out sequentially in the diagnosis approach: acquisition and segmentation of sample images, extraction of specified features, and recognition of missing-bump defects with SOM network.
Repeating the procedures above, we get a sequence of vectors correlating to all microbumps for further analysis.
2.1. Acquisition and segmentation of sample images
2.3. Recognition of missing-bump defects with SOM network
An X-ray system is adopted to capture an image along the direction perpendicular to the plane of microbump array for each sample. Radiation ejected from the source penetrates through the sample, leaving the projection onto the detector. X-ray energy is partially absorbed during transmission. Gray value variance of the images represents the nonuniform material distribution, which is usually caused by heterogeneous materials or inner defects. Then the microbump areas are segmented from the images, so that further analyses and diagnoses of missing-bump defects can be performed. Edge extraction, with the outstanding characteristic of insensitivity to uneven illumination, is adopted for segmentation of microbump areas. Random noises of the images are suppressed through mean filtering. Canny operator with the threshold optimized by testing is used to detect the edges of the microbumps. Morphological dilation, which reassigns the gray value of each pixel with the maximum gray value of pixels within the area defined by the structure element, is performed to connect the extracted unclosed edges. The edge frames are filled. Morphological erosion, the opposite operation to dilation, is performed to remove false edges induced by dilation. We obtain the binary masks with the same size of the original images, where the white pixels with value 1 relate to microbump areas and the black pixels with value 0 relate to background areas. Multiplication of the binary masks and the corresponding original microbump images are carried out, and the microbumps are segmented individually.
We employ the SOM neural network for missing-bump defects recognition. The SOM network, firstly proposed by Kohonen in 1980s, is an unsupervised learning and competing neural network which maps the data set from a high-dimensional input space to a low-dimensional map space, meanwhile, preserving the topology structure [25]. This ANN includes two layers of neural units: the input layer and the output layer (also the competitive layer) as shown in Fig. 1. When the data set for network training is inputted, the competitive layer learns the inner topology structure automatically through Kohonen learning algorithm. The algorithm is performed through an iterative process. Competitions arise in the competitive layer when the data for training is inputted. The weight vectors of the winning neuron and neighboring neurons are updated to pull themselves closer to the input data [26]. The procedures are repeated until the preset stop criterion is triggered, meaning that the network training is over. The weight
2.2. Extraction of specified features Features of the microbump areas are the foundation for distinguishing defective bumps from normal ones. Selection of proper features in spatial domain according to characters of microbumps is particularly important. Typical features will help to reach more accurate recognition and diagnosis results. In our work, four representative features including the maximum gray maxi, the minimum gray mini, the average gray μi, and the standard deviation σi are extracted from every microbump: The maximum gray maxi is defined by:
maxi ¼ faja ≥b; a ∈ Gi ½n; b ∈ Gi ½ng
ð1Þ
Fig. 1. A schematic of SOM neural network structure.
Please cite this article as: G. Liao, et al., Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration, Microelectronics Reliability (2015), http://dx.doi.org/10.1016/j.microrel.2015.09.009
G. Liao et al. / Microelectronics Reliability xxx (2015) xxx–xxx
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Fig. 2. The fabrication flow of microbumps.
vectors of the trained network effectively reflect the clustering characteristic of the input data. We apply the trained SOM neural network for clustering new input data. The result is displayed by a distance mapping method, which recalculates the positions of the output layer neurons in overall consideration of the original coordinates and the Euclidean distances between the weight vectors of the neural units and the input vectors [27]. Compared to the commonly used U-matrix method, the distance mapping method presents a more intuitive visualization.
3. Experimental investigation The samples were designed and fabricated in our lab, as depicted schematically in Fig. 2a-f. Since we only focused on the diagnosis of missing-bump defects in 3D integration with no consideration of electrical properties, the electrical layer was omitted. A 200 nm thick titanium adhesion layer and a 500 nm thick copper seed layer were sputtered in sequence on each cleaned silicon wafer. Then the lithography process was utilized to form the pattern of microbump array with the diameter
Fig. 3. SEM pictures of microbump arrays (a) pattern of the complete microbump array; (b)–(d) patterns of the incomplete microbump arrays.
Please cite this article as: G. Liao, et al., Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration, Microelectronics Reliability (2015), http://dx.doi.org/10.1016/j.microrel.2015.09.009
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Fig. 4. X-ray images of the five samples (a)–(b) X-ray images of the normal samples; (c)–(e) X-ray images of the samples with missing-bump defects.
of 70 μm and the pitch of 150 μm. The electroplating experiments were carried out in a home-made electroplating system to grow Cu microbumps with Sn caps in the circular holes. The photoresist was stripped off from the substrate to exposure the microbumps in the end. Microbumps consisting of 15 μm high copper columns and 2 μm thick tin caps were fabricated. Fig. 3 shows the prepared chips with four patterns of microbump arrays, where Fig. 3a corresponds to the electroplated pattern of a complete 11 × 11 microbump array, and Fig. 3b–d correspond to incomplete arrays with microbumps missing. Five pairs of chips were bonded with a flip chip bonder (SUSS MicroTech FC150). The bonding process lasted for 10 min under the temperature of 300 °C and the pressure of 5 MPa. The Cu/Sn solid–liquid inter-diffusion (SLID) occurred, and five samples belonging to two categories were prepared: two normal samples by bonding chips with the same pattern (Fig. 3a); three samples with missing-bump defects introduced artificially by bonding chips with pattern Fig. 3a to patterns Fig. 3b–d, respectively. Using the microfocus X-ray system (YXLON Y.Cheetah) with the resolution of 500 nm, we acquired the sample images as shown in Fig. 4, where samples 1–2 are normal and samples 3–5 are defective. Obviously some microbump areas in Fig. 4c–e are lighter
than the others, which is caused by the missing-bump defects since more radiation can transmit through the defective areas and then be gathered by the detector. After that, accurate segmentation of the microbumps was carried out, as displayed schematically in Fig. 5. This was the foundation for further reliable analyses and predictions of SOM neural network, since feature vectors representative of the microbumps were figured out based on gray values of the microbump areas. Fig. 5a shows a randomly cropped original image for illustration of the segmentation flow. Edges of microbumps were extracted with canny operator, of which a minority were discontinuous as depicted in the inset (Fig. 5b). The discontinuous edges would cause omission of the corresponding microbumps during segmentation, leading to incomplete inspection of the microbumps. Thus, the extracted edges were dilated to form continuous connections (Fig. 5c). Fig. 5d–e demonstrate that the microbump edges were filled and then eroded to remove the false edges induced by the dilation process before. The binary masks for segmentation were formed. Segmentation of the microbump areas were achieved from the original images multiplied by the corresponding masks (Fig. 5f). 605 microbump areas were segmented from the five sample images, and 605 feature vectors were figured out for SOM network training and recognition.
Fig. 5. Flow of the microbump segmentation (a) the original image; (b) microbump edges extracted; (c) microbump edges after dilation; (d) microbump edges been filled; (e) microbump masks; (f) segmented microbumps.
Please cite this article as: G. Liao, et al., Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration, Microelectronics Reliability (2015), http://dx.doi.org/10.1016/j.microrel.2015.09.009
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Fig. 6. Distribution of the microbumps in samples 1–4 and the results of network training and recognition (a) distribution of the normal and defective bumps in samples 1–4 for network training and recognition; (b) U-matrix of the trained network in 3D display; (c) Clustering result of the input data for network training; (d) recognition result by the trained network for bumps from samples 1–4.
Fig. 7. Distribution of the microbumps in sample 5 and the recognition result (a) distribution of the microbumps for recognition in sample 5; (b) recognition result by the trained network for bumps from sample 5.
Please cite this article as: G. Liao, et al., Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration, Microelectronics Reliability (2015), http://dx.doi.org/10.1016/j.microrel.2015.09.009
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4. Results and discussion A SOM network with 11 × 11 neuron units in the output layer is constructed for analysis. Fig. 6a depicts the microbump distribution of samples 1–4 schematically, where the blue symbols represent the normal bumps and the red symbols represent the defective bumps. 426 normal bumps (marked with blue circles) and 28 defective bumps (marked with red circles) are randomly selected for training the network, and the other 20 normal microbumps (marked with solid blue circles) and 10 defective bumps (marked with solid red circles) are used for testing. The U-matrix of the trained network is shown in Fig. 6b, where the X/Y coordinates denote the lattice of the U-matrix, and the Z coordinate relates to the Euclidean distances between the weight vectors of the neighboring trained units. They are all non-dimensional parameters. It is clear that there are two areas distinguished by the peaks: the small area in the right corner corresponds to defective bumps and the large area on the left side corresponds to normal bumps. The distance mapping method provides a more intuitive visualization in Fig. 6c, where the X/Y coordinates denote the non-dimensional mapping positions of the input vectors. Clusters of the defective bumps and the normal bumps, marked with red circles and blue circles respectively, locate at the top left corner and the lower right corner separately. An evident boundary can be observed between the clusters. Thus, all microbumps are classified into the right categories, demonstrating that the SOM network learns the topology structure of the data effectively. The trained SOM neural network is applied for testing. 30 microbumps from samples 1–4 are inputted to the trained SOM network, and the recognition result is shown in Fig. 6d. We can find that 20 solid blue circles corresponding to normal bumps scatter in the lower right blue circle area, and 10 solid red circles corresponding to defective bumps distribute in the top left red circle area. The trained SOM network can recognize the defective and normal microbumps through the clustering areas with no error. This demonstrates that the SOM network can fulfill recognition of microbumps with high accuracy. To further verify the approach, we use the trained SOM network to recognize microbumps from sample 5 (Fig. 7a), where 104 normal microbumps are marked with solid blue circles and 17 defective bumps are marked with solid red circles. The recognition result is shown in Fig. 7b. We can find that 100% recognition accuracy is achieved, where 104 solid blue circles corresponding to normal bumps distribute in the lower right blue circle area and 17 solid red circles corresponding to defective bumps distribute in the top left red circle area, proving the feasibility of using SOM neural network for X-ray inspection of missing-bump defects. As we know, defect inspection of microbumps in real 3D packages is more complex, where half-missing-bumps are probably more common seen defects, electrical layers complicate the background of sample images, and specification of microbumps is more likely to be diverse. Since half-missing-bumps may be quite similar to normal bumps or missingbumps with tiny or almost whole bump-area missing, the recognition accuracy of the inspection approach will decrease. As to the complicated background and non-uniform bump specifications in real 3D packaging images, improvements of the method are needed. Line detection algorithms can be adopted to eliminate the electrical layers from the background, and segmentation can be performed area by area so that bumps of the same specification can be analyzed together. As the scale of 3D packaging keeps shrinking, acquisition of high resolution microbump images will be the main challenge. Nanofocus X-ray inspection systems together with super-resolution algorithms may provide potential. 5. Conclusions Missing-bump defects may lead to failure of 3D integrated devices, thus effective defect inspection is of major importance. We demonstrate SOM network for missing-bump defect detection. Five samples, of
which two are normal while the other three are defective with missingbump defects introduced artificially, are fabricated in the lab. The diagnosis approach consists of three steps: sample image acquisition and segmentation, feature extraction, and missing-bump defects recognition. Xray images of the samples are acquired, 605 microbumps are segmented with Canny operator and morphology operations, and four features based on gray levels of microbumps are extracted for SOM network training and recognition. The network is trained with 454 bumps randomly selected from 4 samples. The U-matrix of the trained SOM network presents two quite distinct areas: the small area corresponding to defective bumps and the large area corresponding to normal bumps. Clusters of the defective bumps and normal bumps are evidently observed with no error through the distance mapping method. These illustrate that the SOM network can learn the inner topology of the input data effectively. The other bumps consisting of 20 normal microbumps and 10 defective microbumps from the same samples are inputted into the trained network for testing, and the result reveals that the network distinguishes the two types of microbumps correctly. The trained SOM network is further verified by recognizing microbumps (104 normal bumps and 17 defective bumps) from another sample, and high recognition accuracy is achieved. These both prove the feasibility of using self-organizing map network for X-ray inspection of missing-bump defects. Acknowledgments The authors are grateful for the financial supports of the National Key Basic Research Special Fund of China (Grant no. 2015CB057205), the National Natural Science Foundation of China (Grant Nos. 51175211, 51175210 and 51222508), and the Program for Changjiang Scholars and Innovative Research Team in University (No. IRT13017). The authors also would like to appreciate YXLON International (Shanghai) for the aids of X-ray images acquisition. References [1] P. Ramm, A. Klumpp, J. Weber, N. Lietaer, M. Taklo, W. De Raedt, T. Fritzsch, P. Couderc, 3D integration technology: status and application development, Proceedings of the IEEE, ESSCIRC 2010, pp. 9–16. [2] M. Koyanagi, T. Fukushima, T. Tanaka, High-density through silicon vias for 3-D LSIs, Proc. IEEE 97 (2009) 49–59. [3] Y. Lv, M. Chen, M. Cai, S. Liu, A reliable Cu–Sn stack bonding technology for 3D-TSV packaging, Semicond. Sci. Technol. 29 (2014) 025003. [4] Y. Zhang, G. Ding, H. Wang, P. Cheng, R. Liu, Optimization of innovative approaches to the shortening of filling times in 3D integrated through-silicon vias (TSVs), J. Micromech. Microeng. 25 (2015) 045009. [5] A. Yu, J.H. Lau, S.W. Ho, A. Kumar, H.W. Yin, J.M. Ching, V. Kripesh, D. Pinjala, S. Chen, C.F. Chan, Three dimensional interconnects with high aspect ratio TSVs and fine pitch solder microbumps, Electronic Components and Technology Conference, 2009. IEEE 2009, pp. 350–354. [6] K.N. Tu, H.Y. Hsiao, C. Chen, Transition from flip chip solder joint to 3D IC microbump: its effect on microstructure anisotropy, Microelectron. Reliab. 53 (2013) 2–6. [7] Y. Sato, H. Miura, Development of a Non-destructive Inspection System for Micro Bump Joints, Electronic Materials and Packaging, 2008, IEEE, 2008 301–304. [8] Z. Xu, T. Shi, X. Lu, G. Liao, Using active thermography for defects inspection of flip chip, Microelectron. Reliab. 54 (2014) 808–815. [9] L. Su, Z. Zha, X. Lu, T. Shi, G. Liao, Using BP network for ultrasonic inspection of flip chip solder joints, Mech. Syst. Signal Process. 34 (2013) 183–190. [10] A. Starikov, Y.S. Ku, D.M. Shyu, P.Y. Chang, W.T. Hsu, In-line Metrology of 3D Interconnect Processes, 8324 (2012) 832411. [11] A.C. Rudack, L.W. Kong, G.G. Baker, Infrared microscopy for overlay and defect metrology on 3D-interconnect bonded wafers, Advanced Semiconductor Manufacturing Conference (ASMC), 2010 IEEE/SEMI, IEEE 2010, pp. 347–352. [12] J.J. Tang, Y.J. Lay, L.S. Chen, L.Y. Lin, TSV/3DIC profile metrology based on infrared microscope image, ECS Trans. 34 (2011) 937–942. [13] R.S.H. Yang, D.R. Braden, G.M. Zhang, D.M. Harvey, An automated ultrasonic inspection approach for flip chip solder joint assessment, Microelectron. Reliab. 52 (2012) 2995–3001. [14] S. Brand, A. Lapadatu, T. Djuric, P. Czurratis, J. Schischka, M. Petzold, Scanning acoustic gigahertz microscopy for metrology applications in three-dimensional integration technologies, J. Micro/Nanolithogr. MEMS MOEMS 13 (2014) 011207. [15] S. Brand, M. Petzold, P. Czurratis, J.D. Reed, M. Lueck, C. Gregory, A. Huffman, J. Lannon, D.S. Temple, High resolution acoustical imaging of high-density-interconnects for 3D-integration, Electronic Components and Technology Conference (ECTC), 2011 IEEE 61st, IEEE, 2011 37–42.
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Please cite this article as: G. Liao, et al., Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration, Microelectronics Reliability (2015), http://dx.doi.org/10.1016/j.microrel.2015.09.009
Contents lists available at ScienceDirect
Microelectronics Reliability journal homepage: www.elsevier.com/locate/mr
Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration Guanglan Liao a, Pengfei Chen a,⁎, Li Du a, Lei Su a, Zhiping Liu b, Zirong Tang a, Tielin Shi a a b
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China School of Logistics Engineering, Wuhan University of Technology, Wuhan, Hubei 430063, China
a r t i c l e
i n f o
Article history: Received 17 May 2015 Received in revised form 1 September 2015 Accepted 10 September 2015 Available online xxxx Keywords: Three-dimensional integration Missing-bump defect X-ray Self-organizing map neural network
a b s t r a c t Three-dimensional integration has been a key technology in scientific research and industrial production of integrated circuits, where microbumps bridge multiple layers of chips. Microbump defect inspection, especially for missing-bump, is of major significance. We introduce self-organizing map network combined with X-ray imaging, and demonstrate a non-destructive method for rapid and effective inspection of missing microbump defects. 2D X-ray images of samples with microbumps are segmented, and vectors consisting of four features as representatives of microbumps are extracted. A self-organizing map network is constructed, and vectors of microbumps selected randomly from four samples are inputted into the network. Clusters of the defective bumps and the normal bumps are distinguished obviously. Then the other microbumps from the same samples are used for testing. The trained network can recognize the defective and normal microbumps through the clustering areas with no error. Microbumps from a different sample are inputted to the network for further verification, and high recognition accuracy is achieved. These prove the feasibility of using self-organizing map network for X-ray inspection of missing-bump defects. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction Electronic devices develop continuously towards smaller volume, lower power consumption, higher performance, more functions, and lower cost as well. Traditional electronic packaging technology has come across critical crises: the signal propagation delay increases significantly, and the feature dimension is pressing towards the physical limit, etc. [1,2]. Three-dimensional (3D) integration, which refers to the system integration technology that stacks and interconnects multilayer chips vertically, has now been generally considered as the most promising solution to this predicament. Combination of high aspect ratio through silicon vias (TSVs) and ultrafine pitch microbumps presents the shortest signal transmission channels between chips, making it the mainstream scheme of 3D integration [3–5]. Microbumps free from defects are of crucial concerns for high quality interconnects, since they are bridges between multiple chips [6]. However, the complexity of structures and difficulty in assembly processes make microbump defects arise easier during manufacturing. Effective and facilitated on-line diagnosis techniques for microbump defects, especially the missing-bump defects, are indispensable for realizing 3D integration with high reliability [7]. Non-contact defect inspection methods gain distinct advantages compared with contact methods since additional harms or even ⁎ Corresponding author. E-mail address: [email protected] (P. Chen).
destructions may happen to samples during contact detection procedures [8,9]. Optical microscopy measurement is the most common inspection technique in semiconductor industry, although it can only be used for surface examination of pre-bonded microbumps, as bonded bumps are hidden between chips which are usually optically opaque [10]. Infrared microscopy, with the ability of penetrating silicon material rather than metal materials, can be adopted for observation of specified processes, such as TSV profiles, wafer overlay alignments, etc. [11,12]. Owing to the strong capability of spreading through multiple materials, ultrasound inspection is of considerable importance to semiconductor quality control [13]. The most advanced scanning acoustic microscope (SAM) equipped with the gigahertz transducer can provide a resolution below 1 μm [14,15], although the necessity of coupling fluid brings lots of practical limitations. Nowadays, microfocus and nanofocus X-ray systems are widely used for various kinds of defects inspection in microelectronic packaging industry [16]. The latest X-ray inspection system has reached a spatial resolution better than 15 nm, and the computer tomography (CT) technique can provide detailed 3D features of detected samples. Undoubtedly, X-ray inspection, with the advantages of deep sensitivity, high stability, and wide applicability, is of great effectiveness for defect inspection in semiconductor industry [17,18]. In future 3D integration, the diameter of a microbump is expected to shrink down to 1 μm, and the density of microbumps will be up to 104/mm2 [19]. Ultra-massive microbumps make traditional detection systems that highly depend on inspectors' skills and constant mind
http://dx.doi.org/10.1016/j.microrel.2015.09.009 0026-2714/© 2015 Elsevier Ltd. All rights reserved.
Please cite this article as: G. Liao, et al., Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration, Microelectronics Reliability (2015), http://dx.doi.org/10.1016/j.microrel.2015.09.009
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concentration unsustainable, raising strong demands for automatic defect recognition and diagnosis techniques [20]. Machine vision, which imparts human intelligence to machines through adaptive learning approaches, has been widely used in manufacturing to evaluate the quality of products as a substitute for human visual inspection [21,22]. Artificial neural networks (ANNs), composed of collections of individual interconnected processing units, are particularly powerful in dealing with non-linear problems, and thus are commonly used for machine vision [23,24]. In this paper, the self-organizing map (SOM) neural network is applied for missing-bump defect inspection in 3D integration. The defects are artificially introduced during preparation of the samples. An X-ray inspection system is employed to capture images of the samples. The microbump areas are segmented based on canny operator and morphology operations. A random collection of microbumps containing normal microbumps and defective microbumps are inputted into the SOM network for training. The other microbumps from the same samples and those from a new sample are fed to the trained SOM network sequentially to verify the feasibility of the approach.
The minimum gray mini is defined by: mini ¼ faja≤b; a ∈ Gi ½n; b ∈ Gi ½ng
ð2Þ
The average gray μi is defined by: μi ¼
n 1X G ðkÞ n k¼1 i
ð3Þ
The standard deviation σi is defined by: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n u1 X 2 σi ¼ t ½G ðkÞ−μ i n k¼1 i
ð4Þ
Here Gi[n] is denoted as the set of gray values of the i-th microbump area consisting of n pixels, in which the gray value of the k-th pixel is denoted by Gi(k). These features are joined together to form a vector Fi which represents the i-th microbump:
2. Diagnosis approach F i ¼ ½maxi ; mini ; μ i ; σ i
ð5Þ
Three steps are carried out sequentially in the diagnosis approach: acquisition and segmentation of sample images, extraction of specified features, and recognition of missing-bump defects with SOM network.
Repeating the procedures above, we get a sequence of vectors correlating to all microbumps for further analysis.
2.1. Acquisition and segmentation of sample images
2.3. Recognition of missing-bump defects with SOM network
An X-ray system is adopted to capture an image along the direction perpendicular to the plane of microbump array for each sample. Radiation ejected from the source penetrates through the sample, leaving the projection onto the detector. X-ray energy is partially absorbed during transmission. Gray value variance of the images represents the nonuniform material distribution, which is usually caused by heterogeneous materials or inner defects. Then the microbump areas are segmented from the images, so that further analyses and diagnoses of missing-bump defects can be performed. Edge extraction, with the outstanding characteristic of insensitivity to uneven illumination, is adopted for segmentation of microbump areas. Random noises of the images are suppressed through mean filtering. Canny operator with the threshold optimized by testing is used to detect the edges of the microbumps. Morphological dilation, which reassigns the gray value of each pixel with the maximum gray value of pixels within the area defined by the structure element, is performed to connect the extracted unclosed edges. The edge frames are filled. Morphological erosion, the opposite operation to dilation, is performed to remove false edges induced by dilation. We obtain the binary masks with the same size of the original images, where the white pixels with value 1 relate to microbump areas and the black pixels with value 0 relate to background areas. Multiplication of the binary masks and the corresponding original microbump images are carried out, and the microbumps are segmented individually.
We employ the SOM neural network for missing-bump defects recognition. The SOM network, firstly proposed by Kohonen in 1980s, is an unsupervised learning and competing neural network which maps the data set from a high-dimensional input space to a low-dimensional map space, meanwhile, preserving the topology structure [25]. This ANN includes two layers of neural units: the input layer and the output layer (also the competitive layer) as shown in Fig. 1. When the data set for network training is inputted, the competitive layer learns the inner topology structure automatically through Kohonen learning algorithm. The algorithm is performed through an iterative process. Competitions arise in the competitive layer when the data for training is inputted. The weight vectors of the winning neuron and neighboring neurons are updated to pull themselves closer to the input data [26]. The procedures are repeated until the preset stop criterion is triggered, meaning that the network training is over. The weight
2.2. Extraction of specified features Features of the microbump areas are the foundation for distinguishing defective bumps from normal ones. Selection of proper features in spatial domain according to characters of microbumps is particularly important. Typical features will help to reach more accurate recognition and diagnosis results. In our work, four representative features including the maximum gray maxi, the minimum gray mini, the average gray μi, and the standard deviation σi are extracted from every microbump: The maximum gray maxi is defined by:
maxi ¼ faja ≥b; a ∈ Gi ½n; b ∈ Gi ½ng
ð1Þ
Fig. 1. A schematic of SOM neural network structure.
Please cite this article as: G. Liao, et al., Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration, Microelectronics Reliability (2015), http://dx.doi.org/10.1016/j.microrel.2015.09.009
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Fig. 2. The fabrication flow of microbumps.
vectors of the trained network effectively reflect the clustering characteristic of the input data. We apply the trained SOM neural network for clustering new input data. The result is displayed by a distance mapping method, which recalculates the positions of the output layer neurons in overall consideration of the original coordinates and the Euclidean distances between the weight vectors of the neural units and the input vectors [27]. Compared to the commonly used U-matrix method, the distance mapping method presents a more intuitive visualization.
3. Experimental investigation The samples were designed and fabricated in our lab, as depicted schematically in Fig. 2a-f. Since we only focused on the diagnosis of missing-bump defects in 3D integration with no consideration of electrical properties, the electrical layer was omitted. A 200 nm thick titanium adhesion layer and a 500 nm thick copper seed layer were sputtered in sequence on each cleaned silicon wafer. Then the lithography process was utilized to form the pattern of microbump array with the diameter
Fig. 3. SEM pictures of microbump arrays (a) pattern of the complete microbump array; (b)–(d) patterns of the incomplete microbump arrays.
Please cite this article as: G. Liao, et al., Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration, Microelectronics Reliability (2015), http://dx.doi.org/10.1016/j.microrel.2015.09.009
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Fig. 4. X-ray images of the five samples (a)–(b) X-ray images of the normal samples; (c)–(e) X-ray images of the samples with missing-bump defects.
of 70 μm and the pitch of 150 μm. The electroplating experiments were carried out in a home-made electroplating system to grow Cu microbumps with Sn caps in the circular holes. The photoresist was stripped off from the substrate to exposure the microbumps in the end. Microbumps consisting of 15 μm high copper columns and 2 μm thick tin caps were fabricated. Fig. 3 shows the prepared chips with four patterns of microbump arrays, where Fig. 3a corresponds to the electroplated pattern of a complete 11 × 11 microbump array, and Fig. 3b–d correspond to incomplete arrays with microbumps missing. Five pairs of chips were bonded with a flip chip bonder (SUSS MicroTech FC150). The bonding process lasted for 10 min under the temperature of 300 °C and the pressure of 5 MPa. The Cu/Sn solid–liquid inter-diffusion (SLID) occurred, and five samples belonging to two categories were prepared: two normal samples by bonding chips with the same pattern (Fig. 3a); three samples with missing-bump defects introduced artificially by bonding chips with pattern Fig. 3a to patterns Fig. 3b–d, respectively. Using the microfocus X-ray system (YXLON Y.Cheetah) with the resolution of 500 nm, we acquired the sample images as shown in Fig. 4, where samples 1–2 are normal and samples 3–5 are defective. Obviously some microbump areas in Fig. 4c–e are lighter
than the others, which is caused by the missing-bump defects since more radiation can transmit through the defective areas and then be gathered by the detector. After that, accurate segmentation of the microbumps was carried out, as displayed schematically in Fig. 5. This was the foundation for further reliable analyses and predictions of SOM neural network, since feature vectors representative of the microbumps were figured out based on gray values of the microbump areas. Fig. 5a shows a randomly cropped original image for illustration of the segmentation flow. Edges of microbumps were extracted with canny operator, of which a minority were discontinuous as depicted in the inset (Fig. 5b). The discontinuous edges would cause omission of the corresponding microbumps during segmentation, leading to incomplete inspection of the microbumps. Thus, the extracted edges were dilated to form continuous connections (Fig. 5c). Fig. 5d–e demonstrate that the microbump edges were filled and then eroded to remove the false edges induced by the dilation process before. The binary masks for segmentation were formed. Segmentation of the microbump areas were achieved from the original images multiplied by the corresponding masks (Fig. 5f). 605 microbump areas were segmented from the five sample images, and 605 feature vectors were figured out for SOM network training and recognition.
Fig. 5. Flow of the microbump segmentation (a) the original image; (b) microbump edges extracted; (c) microbump edges after dilation; (d) microbump edges been filled; (e) microbump masks; (f) segmented microbumps.
Please cite this article as: G. Liao, et al., Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration, Microelectronics Reliability (2015), http://dx.doi.org/10.1016/j.microrel.2015.09.009
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Fig. 6. Distribution of the microbumps in samples 1–4 and the results of network training and recognition (a) distribution of the normal and defective bumps in samples 1–4 for network training and recognition; (b) U-matrix of the trained network in 3D display; (c) Clustering result of the input data for network training; (d) recognition result by the trained network for bumps from samples 1–4.
Fig. 7. Distribution of the microbumps in sample 5 and the recognition result (a) distribution of the microbumps for recognition in sample 5; (b) recognition result by the trained network for bumps from sample 5.
Please cite this article as: G. Liao, et al., Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration, Microelectronics Reliability (2015), http://dx.doi.org/10.1016/j.microrel.2015.09.009
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4. Results and discussion A SOM network with 11 × 11 neuron units in the output layer is constructed for analysis. Fig. 6a depicts the microbump distribution of samples 1–4 schematically, where the blue symbols represent the normal bumps and the red symbols represent the defective bumps. 426 normal bumps (marked with blue circles) and 28 defective bumps (marked with red circles) are randomly selected for training the network, and the other 20 normal microbumps (marked with solid blue circles) and 10 defective bumps (marked with solid red circles) are used for testing. The U-matrix of the trained network is shown in Fig. 6b, where the X/Y coordinates denote the lattice of the U-matrix, and the Z coordinate relates to the Euclidean distances between the weight vectors of the neighboring trained units. They are all non-dimensional parameters. It is clear that there are two areas distinguished by the peaks: the small area in the right corner corresponds to defective bumps and the large area on the left side corresponds to normal bumps. The distance mapping method provides a more intuitive visualization in Fig. 6c, where the X/Y coordinates denote the non-dimensional mapping positions of the input vectors. Clusters of the defective bumps and the normal bumps, marked with red circles and blue circles respectively, locate at the top left corner and the lower right corner separately. An evident boundary can be observed between the clusters. Thus, all microbumps are classified into the right categories, demonstrating that the SOM network learns the topology structure of the data effectively. The trained SOM neural network is applied for testing. 30 microbumps from samples 1–4 are inputted to the trained SOM network, and the recognition result is shown in Fig. 6d. We can find that 20 solid blue circles corresponding to normal bumps scatter in the lower right blue circle area, and 10 solid red circles corresponding to defective bumps distribute in the top left red circle area. The trained SOM network can recognize the defective and normal microbumps through the clustering areas with no error. This demonstrates that the SOM network can fulfill recognition of microbumps with high accuracy. To further verify the approach, we use the trained SOM network to recognize microbumps from sample 5 (Fig. 7a), where 104 normal microbumps are marked with solid blue circles and 17 defective bumps are marked with solid red circles. The recognition result is shown in Fig. 7b. We can find that 100% recognition accuracy is achieved, where 104 solid blue circles corresponding to normal bumps distribute in the lower right blue circle area and 17 solid red circles corresponding to defective bumps distribute in the top left red circle area, proving the feasibility of using SOM neural network for X-ray inspection of missing-bump defects. As we know, defect inspection of microbumps in real 3D packages is more complex, where half-missing-bumps are probably more common seen defects, electrical layers complicate the background of sample images, and specification of microbumps is more likely to be diverse. Since half-missing-bumps may be quite similar to normal bumps or missingbumps with tiny or almost whole bump-area missing, the recognition accuracy of the inspection approach will decrease. As to the complicated background and non-uniform bump specifications in real 3D packaging images, improvements of the method are needed. Line detection algorithms can be adopted to eliminate the electrical layers from the background, and segmentation can be performed area by area so that bumps of the same specification can be analyzed together. As the scale of 3D packaging keeps shrinking, acquisition of high resolution microbump images will be the main challenge. Nanofocus X-ray inspection systems together with super-resolution algorithms may provide potential. 5. Conclusions Missing-bump defects may lead to failure of 3D integrated devices, thus effective defect inspection is of major importance. We demonstrate SOM network for missing-bump defect detection. Five samples, of
which two are normal while the other three are defective with missingbump defects introduced artificially, are fabricated in the lab. The diagnosis approach consists of three steps: sample image acquisition and segmentation, feature extraction, and missing-bump defects recognition. Xray images of the samples are acquired, 605 microbumps are segmented with Canny operator and morphology operations, and four features based on gray levels of microbumps are extracted for SOM network training and recognition. The network is trained with 454 bumps randomly selected from 4 samples. The U-matrix of the trained SOM network presents two quite distinct areas: the small area corresponding to defective bumps and the large area corresponding to normal bumps. Clusters of the defective bumps and normal bumps are evidently observed with no error through the distance mapping method. These illustrate that the SOM network can learn the inner topology of the input data effectively. The other bumps consisting of 20 normal microbumps and 10 defective microbumps from the same samples are inputted into the trained network for testing, and the result reveals that the network distinguishes the two types of microbumps correctly. The trained SOM network is further verified by recognizing microbumps (104 normal bumps and 17 defective bumps) from another sample, and high recognition accuracy is achieved. These both prove the feasibility of using self-organizing map network for X-ray inspection of missing-bump defects. Acknowledgments The authors are grateful for the financial supports of the National Key Basic Research Special Fund of China (Grant no. 2015CB057205), the National Natural Science Foundation of China (Grant Nos. 51175211, 51175210 and 51222508), and the Program for Changjiang Scholars and Innovative Research Team in University (No. IRT13017). The authors also would like to appreciate YXLON International (Shanghai) for the aids of X-ray images acquisition. References [1] P. Ramm, A. Klumpp, J. Weber, N. Lietaer, M. Taklo, W. De Raedt, T. Fritzsch, P. Couderc, 3D integration technology: status and application development, Proceedings of the IEEE, ESSCIRC 2010, pp. 9–16. [2] M. Koyanagi, T. Fukushima, T. Tanaka, High-density through silicon vias for 3-D LSIs, Proc. IEEE 97 (2009) 49–59. [3] Y. Lv, M. Chen, M. Cai, S. Liu, A reliable Cu–Sn stack bonding technology for 3D-TSV packaging, Semicond. Sci. Technol. 29 (2014) 025003. [4] Y. Zhang, G. Ding, H. Wang, P. Cheng, R. Liu, Optimization of innovative approaches to the shortening of filling times in 3D integrated through-silicon vias (TSVs), J. Micromech. Microeng. 25 (2015) 045009. [5] A. Yu, J.H. Lau, S.W. Ho, A. Kumar, H.W. Yin, J.M. Ching, V. Kripesh, D. Pinjala, S. Chen, C.F. Chan, Three dimensional interconnects with high aspect ratio TSVs and fine pitch solder microbumps, Electronic Components and Technology Conference, 2009. IEEE 2009, pp. 350–354. [6] K.N. Tu, H.Y. Hsiao, C. Chen, Transition from flip chip solder joint to 3D IC microbump: its effect on microstructure anisotropy, Microelectron. Reliab. 53 (2013) 2–6. [7] Y. Sato, H. Miura, Development of a Non-destructive Inspection System for Micro Bump Joints, Electronic Materials and Packaging, 2008, IEEE, 2008 301–304. [8] Z. Xu, T. Shi, X. Lu, G. Liao, Using active thermography for defects inspection of flip chip, Microelectron. Reliab. 54 (2014) 808–815. [9] L. Su, Z. Zha, X. Lu, T. Shi, G. Liao, Using BP network for ultrasonic inspection of flip chip solder joints, Mech. Syst. Signal Process. 34 (2013) 183–190. [10] A. Starikov, Y.S. Ku, D.M. Shyu, P.Y. Chang, W.T. Hsu, In-line Metrology of 3D Interconnect Processes, 8324 (2012) 832411. [11] A.C. Rudack, L.W. Kong, G.G. Baker, Infrared microscopy for overlay and defect metrology on 3D-interconnect bonded wafers, Advanced Semiconductor Manufacturing Conference (ASMC), 2010 IEEE/SEMI, IEEE 2010, pp. 347–352. [12] J.J. Tang, Y.J. Lay, L.S. Chen, L.Y. Lin, TSV/3DIC profile metrology based on infrared microscope image, ECS Trans. 34 (2011) 937–942. [13] R.S.H. Yang, D.R. Braden, G.M. Zhang, D.M. Harvey, An automated ultrasonic inspection approach for flip chip solder joint assessment, Microelectron. Reliab. 52 (2012) 2995–3001. [14] S. Brand, A. Lapadatu, T. Djuric, P. Czurratis, J. Schischka, M. Petzold, Scanning acoustic gigahertz microscopy for metrology applications in three-dimensional integration technologies, J. Micro/Nanolithogr. MEMS MOEMS 13 (2014) 011207. [15] S. Brand, M. Petzold, P. Czurratis, J.D. Reed, M. Lueck, C. Gregory, A. Huffman, J. Lannon, D.S. Temple, High resolution acoustical imaging of high-density-interconnects for 3D-integration, Electronic Components and Technology Conference (ECTC), 2011 IEEE 61st, IEEE, 2011 37–42.
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Please cite this article as: G. Liao, et al., Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration, Microelectronics Reliability (2015), http://dx.doi.org/10.1016/j.microrel.2015.09.009