Effective decapsulation of copper wire-bonded microelectronic devices for reliability assessment

Copper wire bond remains a challenge especially on bond pads with sensitive pad metallization. Extensive work and analysis are needed at the onset of the packaging development phase to meet the right level of manufacturability and reliability requirement. Wire bond related issue is not uncommon even after qualification and after years in high volume manufacturing. The process development gets even tougher for finer wire diameter, bond pads with less than 1μm aluminum thickness and of higher via density structure. A workable wire bond process window is needed upfront. Key is to have a window defined to balance between pad cratering and lifted ball bond due to the harder property of the copper wire. Wire bond process optimization commonly focus around bond time, bond force and bond power (USG). Other factors generally evaluated for copper wire bonding include incoming bond pad cleanliness, bonding pad surface oxidation, wire oxidation during EFO and forming gas flow rate. A lesser known variable, die attach, is often overlooked in packaging or gold to copper wire conversion. Die attach material out gassing at cure is a common hypothesis whenever there is bond pad surface contamination issue. But with the advancement in technology and new material development, out gassing risk is low if the material is cured per the supplier recommendation. Therefore, investigation on weak bond is generally focused more on the wire bond process and incoming bonding pad condition rather than the die attach material. In this paper, investigation on non stick and copper wire bond was carried out. A well defined copper wire bond process on sensitive bonding pads is developed using statistical approach in the form of design of experiment (DOE) and response surface methodology (RSM). Surface analysis methodology using SEM, EDS and AES were also utilized to check for bond pad contamination. To understand deeper into the issue and mechanics of the failure, process mapping and brainstorming sessions have also been carried out. Variables involving wafer saw, pre-wire bond plasma, different batches of wafers, die attach bond line thickness and tilt variation have all been studied thoroughly. Eventually, the root cause of copper wire bond issue been identified to be die attach material related. A strong correlation has been found between lifted wire and the die attach material modulus. There is no more lifted ball concern after changing of the die attach material to a material of a suitable modulus. There is no significant difference in the die attach bond line thickness, coverage and voids among others, comparing with the original die attach material. All the wire bond responses including pad cratering, Al-Cu intermetallic (IMC), wire pull and ball shear have been validated to have no issue at a larger sample size. Reliability assessment in the form of preconditioning, temperature cycling and bias HAST have also passed with no sign of interfacial mechanical failure or test failure. The modulus of elasticity of the die attach material is important and must be a key consideration in copper wire qualification. The elasticity of the material changes from room temperature to copper wire bonding temperature. A low modulus at the bonding temperature can cause wire bondability problem such as lifted ball bond due to ultrasonic power loss during ball bonding. Therefore, selection of a die attach material with a suitable range of modulus of elasticity is critical in the integrity of copper wire bonding.

The rapid ramping of fine pitch copper wire bonding production is a perfect example of a market driven technology implementation. The explosive increase in gold commodity pricing has paved the road for this conversion. Several challenges known from thick copper wire bonding had to be overcome to make this technology viable technically and economically. Some fundamental studies of copper wire bonding to the full spectrum of die surfaces and wafer nodes was available at the beginning of manufacturing implementation but over the last year, a large number of research papers have been published greatly enhancing the basic understanding of mechanism, metallurgy and chemistry of copper wire bonding. Equipment manufacturers have developed dedicated copper wire bonders and tool kits, wire manufacturers have developed softer copper wires as well as oxidation resistant wires, mold compound suppliers have developed low corrosion compounds and wire bond engineers have learned to optimize the bonding process to yield cost effective and reliable copper wire bond die packages. Here, high volume manufacturing experiences of copper wire bond packaging and long term reliability data will be presented.

Accurate identification and classification of atmospheric particulates can provide the basis for their source apportionment. Most current research studies mainly focus on the classification of atmospheric particles based on the energy spectrum of particles, which has the problems of low accuracy and being time-consuming. It is necessary to study the classification method of atmospheric particles with higher accuracy. In this paper, a convolutional neural network (CNN) model with attention mechanism is proposed to identify and classify the scanning electron microscopy (SEM) images of atmospheric particles. First, this work established a database, Qingdao 2016–2018, for atmospheric particles classification research. This database consists of 3469 SEM images of single particulates. Secondly, by analyzing the morphological characteristics of single particle SEM images, it can be divided into four categories: fibrous particles, flocculent particles, spherical particles, and mineral particles. Thirdly, by introducing attention mechanism into convolutional neural network, an Attention-CNN model for the identification and classification of the four types of atmospheric particles based on the SEM images is established. Finally, the Attention-CNN model is trained and tested based on the SEM images database, and the results of identification and classification for four types of particles are obtained. Under the same SEM images database, the classification results from Attention-CNN are compared with those of CNN and SVM. It is found that Attention-CNN has higher classification accuracy and reduces significantly the misclassification number of particles, which shows the focusing effect of attention mechanism.

In order to perform nano-manipulation more accurately, it is necessary to study the relationship between the scanning electron microscope (SEM) image and the actual length. In this paper, based on the secondary electron image, the displacement of 1.81nm in SEM was measured the actual displacement was obtained according to the change of the pixels, so as to realize the nanometer displacement calibration and real-time measurement of the nano robot manipulator in SEM. According to the magnification, resolution and scale bar of SEM image, the mapping relationship between pixels and actual length was established. The obvious feature point and area in the SEM image were selected to be marked. The driving signal was applied to the manipulator of the nano robot, and the template matching algorithm was used to track the feature point and area in real time. The change of the feature point in the SEM image was obtained, and the actual displacement of the manipulator was calculated according to the mapping relationship between the pixel points and the actual displacement. We calibrated the displacement of the nanorobots and measured the actual displacement in real time, which laid a foundation for the precise control of the nanorobots in the future.

Image noise is an important factor that influences the accuracy of strain field measurements by means of digital image correlation and scanning electron microscope (SEM) imaging. We propose a new model to quantify the SEM image noise, which extends the classical photon noise model by taking into account the brightness setup in SEM imaging. Furthermore, we apply this model to investigate the impact of different SEM setting parameters on image noise, such as detector, dwell time, spot size, and pressure in the SEM chamber in the context of low vacuum imaging.

During the past two years copper wire bonding has entered high volume manufacturing at a number of leading edge OSATs and IDMs. Usage of copper wire has achieved 20% market share and is expected to exceed 50% within three years. Products spanning the range from low pin count devices with relatively large wire diameter to FPGA's with nearly one thousand wires at 20 μm or even 18 μm wire are now using copper wire. This paper will discuss the requirements for developing a robust copper wire bonding process and then moving it to high volume manufacturing. Process optimization begins with the selection of the appropriate wire diameter, ball diameter, bonding tool and bonding process type. These are functions not only of the bond pad opening, but also of the pad aluminum thickness and relative sensitivity of the pad to damage. Proper optimization depends on the availability of new and modified bond quality metrologies, such as extensive reliance on cross-sectioning and intermetallic coverage measurements. The bonding window of a copper wire bonding process is defined in substantially new terms compared to optimization in gold wire bonding. Once an optimized process has been developed in the lab on a single bonder, it needs to be verified. Copper wire bond processes are much less forgiving with respect to the acceptable variability on the manufacturing floor. To ensure that the process is stable, a low volume pre-manufacturing test is highly recommended. This not only makes sure that the process is stable across multiple bonders, but also ensures the adequacies of manufacturing controls, incoming materials quality and sufficient equipment calibration and maintenance procedures.

During the past ten years, copper (Cu) bond wires have increasingly replaced gold (Au) wire materials. While this development began in the consumer electronics sector, Cu wires are now increasingly advancing into applications with challenging environmental conditions and high reliability requirements, such as the automotive sector. In the meantime, several types of high reliability Cu wires are available in the market. However, there are only few papers describing/comparing the effect of the additive element used in the Cu bulk material. Thus, we investigated the failure and improving mechanisms of Cu wire bond contacts with different type of additive elements. Subsequent to the challenging artifact-free preparation routines, high resolution analyses (SEM, transmission electron microscopy - TEM, nanospot-EDS) were carried out on ball bond contacts to clarify the effect of additive elements to the reliability of the wire bonded contacts especially during HAST. The result s of this investigation will be valuable information for the wire users in selecting the optimal wire material required for automotive devices.

As the technology node shrinks, the systematic defects such as the missing and extra pattern are generated in the process of cell patterning. The defect such as the extra and missing pattern can lead to a critical failure on device. Subsequently, the systematic defect in the UBE could be an etch-gas path on proceeding the etch process and cause the quality degradation of device. In this paper, we propose an automated system to inspect the systematic defect in the cell array with the scanning electron microscope (SEM) image and the physical design. The method consists of conversion, matching, and detection. In the conversion method, the SEM image is converted into the layout using image processing such as noise reduction, segmentation and contour tracing. In the following matching method, gradient descent optimization is used to match the coordinate of layout converted from the SEM image with the coordinate of physical design. In the final detection method, the defect is detected by inspecting the patterns of two layouts. On the test of 2,500 data, we confirmed that the accuracy improved from 97% to 100% as a result of comparing the engineer's visual inspection method with the proposed method. In addition, the turnaround time (TAT) is improved by approximately 40 times. The proposed system is currently applied to DRAM products and used for the field of design for manufacturing (DFM) and manufacturing process.

Evaluating the ability of energy dispersive X-ray analysis to monitor binding oil content of carbon paste electrodes exposed to biofouling agents

This paper presents a study of micro-scale deformation of materials utilizing scanning electron microscopy (SEM) images and the digital image correlation (DIC) technique. A loading stage was integrated into the SEM imaging system. During the experiment, a series of SEM images of the specimen were acquired in situ. The DIC technique was then applied to these SEM images to calculate the displacement and strain field at the area of interest. Additional surface preparation may be needed in order to have an effective pattern for DIC analysis. Two applications are presented in the paper. Using small tensile specimens, the mechanical properties of electrodeposited nickel-based LIGA (an acronym from German words for lithography, electroplating, and moulding) specimens were characterized. In this case, the natural microstructural feature of the specimen surface was used directly as the pattern for DIC analysis. This method was also applied to study the strain concentration around the crack tip during the ductile fracture test of Al 6061-T651. In contrast to the previous case, the DIC patterns were generated by sputtering a thin layer of gold film on to the specimen surface through the copper mesh grid.

Nanomaterials, owing to their distinctive features, are crucial across numerous scientific domains, especially in materials science and nanotechnology. Precise segmentation of Scanning Electron Microscope (SEM) images is essential for evaluating attributes such as nanoparticle dimensions, morphology, and distribution. Conventional image segmentation techniques frequently prove insufficient for managing the intricate textures of SEM images, resulting in a laborious and imprecise process. In this research, a modified U-Net architecture is presented to tackle this challenge, utilizing a ResNet50 backbone pre-trained on ImageNet. This model utilizes the robust feature extraction abilities of ResNet50 alongside the effective segmentation performance of U-Net, hence improving both accuracy and computational efficiency in TiO2 nanoparticle segmentation. The suggested model was assessed using performance metrics including accuracy, precision, recall, IoU, and Dice Coefficient. The results indicated a high segmentation accuracy, demonstrated by a Dice score of 0.946 and an IoU of 0.897, with little variability reflected in standard deviations of 0.002071 and 0.003696, respectively, over 200 epochs. The comparison with existing methods demonstrates that the proposed model surpasses previous approaches by attaining enhanced segmentation accuracy. The modified U-Net design serves as an excellent technique for accurate nanoparticle segmentation in SEM images, providing substantial enhancements compared to traditional approaches. This progress indicates the model's potential for wider applications in nanomaterial research and characterization, where precise and efficient segmentation is essential for analysis.

The MeX™ software is a useful tool for tridimensional data collection for surface evaluation and could be relevant to evaluate the same specimen in different phases of the study, assuming repeated measures of dental enamel roughness. The aim of this study was to evaluate the influence of sample metallization for dental enamel roughness analysis with 3D images reconstructed using MeX™ software from Scanning Electron Microscopy (SEM) images. The influence of 74.98% (%mol/mol) argon-oxygen plasma for carbon layer removal on surface roughness of the metallized specimen was also evaluated. Dental enamel specimens were prepared for SEM analysis with and without carbon metallization using conventional or environmental modes. Argon-oxygen plasma for carbon layer removal was used and surface roughness was re-evaluated. Roughness obtained by SEM and MeX™ reconstructed images, with or without metallization, did not differ. No significant alteration on surface roughness after carbon layer removal using plasma was found. SEM baseline evaluation using conventional mode without sample preparation and in environmental mode were not comparable. Roughness of enamel 3D images reconstructed with MeX™ software from SEM images, with or without metallization was similar. The 74.98% (%mol/mol) argon-oxygen plasma removed the carbon layer with no effect on enamel roughness.

A methodology for the automated analysis of metal powder scanning electron microscope (SEM) images towards material characterization is developed and presented. This software-based tool takes advantage of a combination of recent artificial intelligence advances (mask R-CNN), conventional image processing techniques, and SEM characterization domain knowledge to assess metal powder quality for additive manufacturing applications. SEM is being used for characterizing metal powder alloys, specifically by quantifying the diameter and number of spherical particles, which are key characteristics for assessing the quality of the analyzed powder. Usually, SEM images are manually analyzed using third-party analysis software, which can be time-consuming and often introduces user bias into the measurements. In addition, only a few non-statistically significant samples are taken into consideration for the material characterization. Thus, a method that can overcome the above challenges utilizing state-of-the-art instance segmentation models is introduced. The final proposed model achieved a total mask average precision (mAP50) 67.2 at an intersection over union of 0.5 and with prediction confidence threshold of 0.4. Finally, the predicted instance masks are further used to provide a statistical analysis that includes important metrics such as the particle size distinction.

In our experiment, the possibility of forming copper wire bonds in Chip-on-Board (COB) applications was investigated. In the experiment, such surface finishes were studied and compared which are common in printed circuit board based COB applications and can be bonded with wire bonding technologies. These finishes were immersion silver (ImAg), electroless nickel / immersion gold (ENIG) and electroless nickel / electroless palladium / immersion gold (ENEPIG). On each type of surface finish, copper wires with two diameters (33 and 300 μm) were bonded with a TPT Ultrasonic bonder. The wire bonds then characterized by optical inspections and mechanical measurements which are detailed in the paper.

In this paper, we propose an accurate automatic defect detection method using the quadtree decomposition on scanning electron microscope (SEM) images. The proposed method consists of five steps including alignment between the inspection and reference images, edge relaxation, defect candidate detection, multichannel integration, and defect scoring. Since the periodicity of semiconductor SEM images is usually high, alignment can be failed due to a lot of local minima. To resolve this problem, we propose the enhancement method of non-periodic areas in SEM images. Then, we propose an edge relaxation technique to minimize the falsely remained edges in the aligned and subtracted image due to different focusing and exposure between two images. Then, the defect candidates are detected by analyzing the inhomogeneity based on quadtree decomposition, which is enhanced by our outlier-robust decomposition rule and adaptive decomposition thresholding to prevent over/under decomposition. Whereas the conventional defect detection system focuses only on the intensity of defects, our method considers both the inhomogeneity and intensity of defects. Field test results demonstrated that the proposed method provided more accurate defect detection with faster speed as compared to the conventional system. The proposed method can much reduce the automatic defect review rework compared with the conventional system.