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High sensitivity detection of ultrasonic signal for nondestructive inspection using pulse compression method
Microelectronics Reliability 92 (2019) 172–178
Contents lists available at ScienceDirect
Microelectronics Reliability journal homepage: www.elsevier.com/locate/microrel
High sensitivity detection of ultrasonic signal for nondestructive inspection using pulse compression method
T
⁎
Hiroki Mitsuta , Kaoru Sakai Research & Development Group, Hitachi, Ltd., 292, Yoshida-cho, Totsuka-ku, Yokohama-shi, Kanagawa-ken 244-0817, Japan
A R T I C LE I N FO
A B S T R A C T
Keywords: Ultrasonic inspection Scanning acoustic microscopy Defect detection Pulse compression Correlation function
Semiconductor parts are currently being stacked and miniaturized, and as device structures become more complicated, nondestructive inspections are increasingly needed for improving yield and securing reliability. Scanning acoustic microscopy (SAM) is used to detect delamination and voids. However, when the frequency of the ultrasonic probe is increased to improve the spatial resolution, conventional SAM cannot detect defects in deep parts because ultrasonic attenuation increases. In this research, we developed high sensitivity detection technique of ultrasonic signal using a pulse compression method, examined a modulation method, and designed the waveform of the transmitted signal to achieve high defect detection sensitivity. Conventional and developed detection techniques of ultrasonic signal were applied to a 3-layer stacked specimen, and their ultrasonic images, noise reduction effect, and defect detection sensitivity were compared.
1. Introduction For semiconductors or electronic components used in PCs and mobile devices, nondestructive inspection of bonding defects is required to improve yield and reliability. Scanning acoustic microscopy (SAM) has been widely used as a nondestructive inspection method for detecting defects such as voids and internal delamination [1]. The principle of generating ultrasonic images using SAM is shown in Fig. 1. In conventional SAM, a pulse wave is irradiated to a specimen by an ultrasonic probe. As shown in Eq. (1), a part of the transmitted signal is reflected at a boundary that has different acoustic impedances such as a surface, bonding interfaces, and defects in the specimen.
R=
Z2 − Z1 Z2 + Z1
(1)
Here, R is reflectance of sound pressure, and Z1 and Z2 are acoustic impedances of the material 1 and 2 in Fig. 1, respectively. The ultrasonic signal reflected from the specimen is received at an ultrasonic probe, and amplitude of the received signal corresponding to the interface to be visualized is converted into a gray level pixel for creating an ultrasonic image. The ultrasonic image of a specific interface in the specimen can be acquired by repeating these operations at each measurement point in the observation area. Here, since the acoustic impedance of gas in the bonding defect such as voids or internal delamination is much smaller than that of solid materials [2], amplitude of
⁎
Corresponding author. E-mail address: [email protected] (H. Mitsuta).
https://doi.org/10.1016/j.microrel.2018.12.002 Received 6 September 2018; Accepted 2 December 2018 Available online 13 December 2018 0026-2714/ © 2018 Elsevier Ltd. All rights reserved.
the reflected signal in defects area is increased. Therefore, it is possible to detect defect areas from the difference in brightness value in the ultrasonic images. Semiconductor parts are currently being stacked and miniaturized as semiconductor products are becoming more multifunctional, faster, larger in capacity, and smaller in size [3]. For this reason, detection sensitivity must be improved to detect smaller defects at each interface. The defect detection sensitivity is affected by a beam diameter corresponding to a spatial resolution and ultrasonic energy corresponding to detectable depth. The beam diameter of the focused ultrasonic beam (d) and ultrasonic energy of the received signal (E) are calculated in Eqs. (2) and (3) [4,5].
d=a
vF fD
E = E0 exp(−2bf 2x )
(2) (3)
Here, a and b are constants, λ is a wavelength at peak frequency of the ultrasonic wave, F is a focal length of an acoustic lens, D is a diameter of the ultrasonic probe, E0 is initial ultrasonic energy, and x is a propagation distance of the ultrasonic beam. As shown in Eq. (2), the spatial resolution can be increased by applying the ultrasonic probe with higher frequency. As an example of this, gigahertz (GHz) SAM is used to detect defects several micrometers near the surface with the spatial resolution in 1 μm. On the other hand, as shown in Eq. (3), as the frequency of the ultrasonic probe increases, ultrasonic energy
Microelectronics Reliability 92 (2019) 172–178
H. Mitsuta, K. Sakai
Fig. 1. Principle of generating ultrasonic image using SAM.
increases only at the reflection point and becomes small in other points, so that it is robust to noise and a high signal-to-noise ratio (SNR). In the pulse compression method, the transmitted signal to be irradiated to the specimen greatly affects the pulse compression ratio. Therefore, we examined a modulation method effective for effectively improving the detection sensitivity and designed an optimum waveform. The conventional modulation methods are summarized in Fig. 3. First, the chirp modulation method is generated by continuously increasing or decreasing the frequency with time, which has an advantage that the waveform can be designed with two parameters: a frequency bandwidth (Δf) and a signal length (Δt). Here, the piezoelectric element used for converting the voltage signal into the ultrasonic signal has non-uniform frequency characteristics, so the waveform of the output ultrasonic signal is distorted and the designed voltage waveform cannot be output. When pulse compression is performed with the distorted waveform, the pulse compression ratio becomes lower than the ideal waveform, making it difficult to detect the reflected signal. Since the chirp modulation method that continuously changes the frequency needs to use a frequency band with high and low gain, the distortion owing to frequency characteristics is large, and waveform distortion is difficult to compensate for because there are few parameters. Next, the phase shift keying (PSK) method assigns signals of different phases to a given code string [7]. The advantage of this method is it can select frequencies with high gain since it can create the waveform with a single frequency, so the waveform distortion owing to frequency characteristics is low. However, since high frequency noise is generated at a phase switching point (arrows in Fig. 3), the designed signal is difficult to output, the same as in chirp modulation. Next, the frequency shift keying (FSK) method assigns signals of different frequencies to a given code string [7]. This method has the advantage that it can design waveforms at a few frequencies that have high gain, but the waveform takes much time to optimize because there are many parameters. However, FSK has larger technical advantages than other methods, so we decided to use FSK as the modulation method for pulse compression.
attenuates exponentially, and the detectable depth becomes shallow. Because of that, the spatial resolution and detectable depth are in a trade-off relationship, so minute defects in a deep part are difficult to detect by conventional SAM using pulse waves. In this research, we developed a technique that detects the attenuated ultrasonic signal with high sensitivity by signal processing and improves defect detection sensitivity in deep parts of the specimen. 2. Detection sensitivity improvement technique using pulse compression method To improve defect detection sensitivity, we applied a pulse compression method used to improve distance resolution in radar signal processing [6]. The pulse compression method is a signal processing technique that computes a correlation function between a signal transmitted to the specimen and a signal received at the ultrasonic probe. The correlation function is calculated as follows.
C (lT ) =
1 N
N
∑ u (nT ) v ((n + l) T ), n=1
(l = 0, 1, …, L − 1) (4)
Here, T is the sampling time interval, u(nT) is the received signal and v (nT) is the transmitted signal. A schematic image of pulse compression is shown in Fig. 2. In the pulse compression method, a modulation signal such as a pseudo-random number is used as the transmitted signal (Fig. 2(a)), and the received signal reflected from each interface (S0, S1, and S2 in Fig. 1) is acquired by the ultrasonic probe (Fig. 2(b)). When the modulation signal is transmitted to the specimen shown in Fig. 1, the received signal including three reflection waves can be acquired, but no reflected point can be distinguished in the received signal shown in Fig. 2(b). Here, when the correlation function is calculated between the transmitted and received signals, each reflected point is visualized (Fig. 2(c)) because the correlation function takes only a high value when the same waveform component with the transmitted signal exists in the received signal. In this way, the pulse compression method has the advantage that the correlation function
Fig. 2. Schematic images of pulse compression method. 173
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Fig. 3. Comparison of modulation methods.
3. Experiments The specimen was formed by stacking three layers of a 60-μm-thick silicon (Si) wafer and a 20-μm-thick polyimide film on a 400-μm-thick Si wafer and processing holes as the artificial defects in the polyimide film of the third layer from the top. The defects were square holes with sizes of 100 (2 lines), 50 (3 lines), 40 (3 lines), 30 (4 lines), 20 (5 lines), 10 (5 lines), 8 (10 lines), 6 (10 lines), 4 (15 lines), 2 (30 lines), and 1 (30 lines) μm as shown in Fig. 5. The specimen was ultrasonically inspected by using an ultrasonic probe having a nominal frequency of 200 MHz. The waveform of FSK was designed using sinusoidal waves with 100 and 200 MHz, and parameters of FSK were optimized by the method described in Fig. 4. The ultrasonic image was acquired by using a C-scan method [8] for scanning the ultrasonic probe two-dimensionally in the in-plane direction of the specimen. The scanning interval of the two-dimensional scan was set to 2 μm, and the observation area was 720 μm × 2800 μm. The conventional detection technique of ultrasonic signal using pulse waves (hereinafter, referred to as pulse waves) obtained the ultrasonic image by converting the amplitude of the reflection signal from the interface to be imaged into a brightness value. The proposed detection technique of ultrasonic signal using the pulse compression method (hereinafter, referred to as pulse compression) obtained the correlation function by calculating the transmission signal for the reflected signal from each interface in the specimen and obtained the ultrasonic image by converting the correlation function into the brightness value. To evaluate the difference in defect detection sensitivity between the conventional and proposed detection techniques of ultrasonic signal, ultrasonic images were acquired by using pulse waves and pulse compression.
Fig. 4. Optimization process of FSK waveform.
As discussed previously, to achieve high defect detection sensitivity, the transmission waveform must be suitably designed. The waveform design concept is shown in Fig. 4. First, the FSK waveform is created that combines multiple sinusoidal waves by inputting initial parameters. Next, the distorted waveform calculated by convoluting the frequency characteristic of the piezoelectric element to the designed waveform is created. Then, the autocorrelation function of the distorted waveform is calculated and the pulse width is obtained from full width at half maximum of the correlation function. By using the calculated pulse width as an evaluation function, parameters of FSK were optimized so that the evaluation function would be minimized by using the simulated annealing method. We evaluated the defect detection sensitivity by applying the optimized waveform to the detection of ultrasonic signal of a multilayer stacked specimen.
4. Results and discussion Fig. 6 shows ultrasonic images with defects ranging from 6 to 100 μm in the third layer bonding interface obtained by using pulse waves and pulse compression. In the ultrasonic image obtained by using pulse waves (Fig. 6(a)), 100 μm defects were observed, but smaller defects were difficult to detect, which is considered to be caused by the variation in brightness value, that is, large noise. In the ultrasonic image obtained by using pulse compression (Fig. 6(b)), the boundaries of the 100 μm defects were clearer than in the image obtained by using pulse waves, and defects smaller than 100 μm could be observed. Fig. 7 shows cross sectional profiles of defects ranging from 100 to 174
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H. Mitsuta, K. Sakai
Fig. 5. Schematic image of specimen.
20 μm. When pulse compression was used, the variation of the brightness value was smaller than when pulse waves were used and the brightness value of the defect parts was actualized. This result indicates that pulse compression reduces noise and improves visibility of the ultrasonic image. This is considered to be due to the fact that the SNR was improved by pulse compression, so the reflected signal from the specimen interface could be detected with high sensitivity. For quantitative evaluation, the defect detection sensitively was evaluated by comparing the standard deviation (σ) of the brightness value in the bonded area and SNR in each defect area of the ultrasonic image. Here, signal values (S) of SNR are defined as the brightness value in each defect part, and noise values (N) are defined as 3σ. Fig. 8 shows A to B cross-sectional profiles of the bonded region. As a result of comparing the cross-sectional profiles, the image obtained by using pulse waves was noisy, and the standard deviation 3σ from the extracted cross sectional profile was 0.03 for pulse waves and 0.01 for pulse compression. Based on these results, pulse compression reduced the noise value by 66%. Fig. 9 shows the relationship between defect size and SNR calculated by ultrasonic images shown in Fig. 6. Pulse compression was found to improve both SNR and the visibility of the ultrasonic image. For the 100 μm defects, SNR was 12.2 for pulse compression and 2.3 for pulse waves. To compare the defect detection sensitivity, ultrasonic images of an unsigned 8-bit integer were binarized. The threshold of binarization was determined under two conditions: a value at which false reports are minimized and a value at which the smallest defects can be detected. Fig. 10 shows the ultrasonic image binarized under the condition at which false reports are minimized (false report rate in the bonded area is less than 1%). Under this condition, pixels judged to be defects in the bonded area (inside the dashed-line box in Fig. 10(a)) were defined as false reports. When pulse waves were used, it was possible to recognize parts of 100 μm and 50 μm defects in the image binarized with the threshold value 187 (red circles in Fig. 10(a)). When pulse compression was used, 100 μm square defects could clearly be recognized in the image binarized with a threshold value of 136, unlike when pulse waves were used (Fig. 10(b)). Furthermore, 100% of 100, 50, 40, and 30 μm defects and 90% of 20 μm detects could be detected. Since the ultrasonic image obtained by using pulse waves
Fig. 6. Ultrasonic images obtained using pulse waves and pulse compression.
175
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H. Mitsuta, K. Sakai
Fig. 7. Cross-sectional profiles in defect areas. Left and right graphs show the results of using pulse wave and pulse compression, respectively.
noise could be reduced by calculating the correlation function, it is considered that the signal of the defect part could be revealed and even smaller defects could be detected. Next, to determine the minimum size of a detectable defect, the threshold of each ultrasonic image was adjusted, and the size of detected defects and the false report rate were evaluated (Fig. 11). The
contained larger noise than that obtained by using pulse compression as shown in Fig. 7, it seems that the threshold needed to be raised to suppress false reports. As a result of raising the threshold when pulse waves were used, the brightness value of the defect part was also buried, and it is considered that most regions containing a 100 μm defect could not be detected. When pulse compression was used, since 176
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H. Mitsuta, K. Sakai
Fig. 8. Cross-sectional profiles of bonded region.
Fig. 9. Comparison between signal-to-noise ratios of ultrasonic inspection methods.
binarized image obtained by using pulse waves was able to reveal two 50 μm defects (Fig. 11(a)). At this condition, the threshold was 165, and the false repot rate was 10.1%. By decreasing the threshold value below that in Fig. 10(a), the area where the defects were recognized expanded in the image obtained by using pulse waves (Fig. 11(a)), but the detectable defect size did not change and the noise increased. In the image obtained by using pulse waves, the signal reflected from the interface was buried in noise, and it seems that the difference between the bonded and the defect areas could not be detected. In the image obtained by using pulse compression (Fig. 11(b)), detection sensitivity improved and 10 μm defects were able to detected, where the threshold was 125 and the false repot rate was 9.8%. Some defects of 8 μm or less could be detected, but they could not be recognized as isolated detects. Because the beam diameter widened due to refraction and diffusion of the ultrasonic beam and the spatial resolution was reduced, it is considered that the signals reflected from the bonded region and the defect did not separate. To investigate the detection limit, it is necessary to evaluate defect detection sensitivity using an isolated defect and perform ultrasonic propagation simulation considering attenuation and beam spread in the material. Fig. 10. Binary images acquired under condition at which false reports are minimized. (For interpretation of the references to colour in this figure, the reader is referred to the web version of this article.)
5. Conclusion We developed a high sensitivity detection technique of ultrasonic signal using the pulse compression method for improving defect detection sensitivity. We examined a modulation method and optimized the waveform of the transmitted signal to achieve a high pulse compression ratio. The ultrasonic inspection was performed using pulse wave and pulse compression methods for a specimen formed by stacking three layers of a 60-μm-thick Si wafer and a 20-μm-thick
polyimide film and processing holes as the artificial defects in the polyimide film of the third layer. The defect detection sensitivity was evaluated by using a standard deviation and signal-to-noise ratio (SNR). The ultrasonic inspection technique using the pulse compression method reduced the noise value by 66% and increased the SNR 177
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[2] L.W. Schmerr, Fundamentals of ultrasonic nondestructive evaluation – a modeling approach, Springer Series in Measurement Science and Technology, 2016. [3] Yole Development, Status of advanced substrates 2018: embedded dies & interconnects, substrate like PCB trends, Market & Technology Report, 2018. [4] Josef Krautkrämer, Herbert Krautkrämer, Ultrasonic Testing of Materials, SpringerVerlag, Berlin Heidelberg, 1983. [5] Z. Remili, Y. Ousten, B. Levrier, E. Suhir, L. Bechou, Scanning acoustic microscopy and shear wave imaging mode performances for failure detection in high-density microassembling technologies, 2015 Electronic Components & Technology Conference, 2015, pp. 2090–2101. [6] Thin Thin Mar, Su Su Yi Mon, Pulse compression method for radar signal processing, Int. J. Sci. Eng. Appl. 3 (2) (2014). [7] R.L. Brewster, W.W.S. Jibrail, Detection of FSK and DPSK data signals by pulse compression, Institution of Electrical Engineers Proceedings, vol. 129, 1982, pp. 273–280. [8] Lili Ma, Shengxiang Bao, Dechun Lv, Zhibo Du, Shilan Li, Application of C-mode scanning acoustic microscopy in packaging, electronic packaging technology, ICEPT 2007, 2007, pp. 1–6.
Fig. 11. Binary images acquired under the condition at which smallest defects can be detected.
compared with the ultrasonic inspection technique using pulse waves. Under a condition of false reports under 1%, 50 μm defects could be detected in images obtained by using the ultrasonic inspection technique using pulse waves whereas 20 μm defects could be detected in images obtained by using the ultrasonic inspection technique using the pulse compression method. This proved that the visibility of defects was improved in the ultrasonic image to which the ultrasonic inspection technique using the pulse compression method was applied. References [1] J.A. Khan, M. Mina, L. Udpa, S.S. Udpa, Analysis of scanning acoustic microscopy images of IC chips, Review of Progress in Quantitative Nondestructive Evaluation, vol. 14, 1995, pp. 915–922.
178
Contents lists available at ScienceDirect
Microelectronics Reliability journal homepage: www.elsevier.com/locate/microrel
High sensitivity detection of ultrasonic signal for nondestructive inspection using pulse compression method
T
⁎
Hiroki Mitsuta , Kaoru Sakai Research & Development Group, Hitachi, Ltd., 292, Yoshida-cho, Totsuka-ku, Yokohama-shi, Kanagawa-ken 244-0817, Japan
A R T I C LE I N FO
A B S T R A C T
Keywords: Ultrasonic inspection Scanning acoustic microscopy Defect detection Pulse compression Correlation function
Semiconductor parts are currently being stacked and miniaturized, and as device structures become more complicated, nondestructive inspections are increasingly needed for improving yield and securing reliability. Scanning acoustic microscopy (SAM) is used to detect delamination and voids. However, when the frequency of the ultrasonic probe is increased to improve the spatial resolution, conventional SAM cannot detect defects in deep parts because ultrasonic attenuation increases. In this research, we developed high sensitivity detection technique of ultrasonic signal using a pulse compression method, examined a modulation method, and designed the waveform of the transmitted signal to achieve high defect detection sensitivity. Conventional and developed detection techniques of ultrasonic signal were applied to a 3-layer stacked specimen, and their ultrasonic images, noise reduction effect, and defect detection sensitivity were compared.
1. Introduction For semiconductors or electronic components used in PCs and mobile devices, nondestructive inspection of bonding defects is required to improve yield and reliability. Scanning acoustic microscopy (SAM) has been widely used as a nondestructive inspection method for detecting defects such as voids and internal delamination [1]. The principle of generating ultrasonic images using SAM is shown in Fig. 1. In conventional SAM, a pulse wave is irradiated to a specimen by an ultrasonic probe. As shown in Eq. (1), a part of the transmitted signal is reflected at a boundary that has different acoustic impedances such as a surface, bonding interfaces, and defects in the specimen.
R=
Z2 − Z1 Z2 + Z1
(1)
Here, R is reflectance of sound pressure, and Z1 and Z2 are acoustic impedances of the material 1 and 2 in Fig. 1, respectively. The ultrasonic signal reflected from the specimen is received at an ultrasonic probe, and amplitude of the received signal corresponding to the interface to be visualized is converted into a gray level pixel for creating an ultrasonic image. The ultrasonic image of a specific interface in the specimen can be acquired by repeating these operations at each measurement point in the observation area. Here, since the acoustic impedance of gas in the bonding defect such as voids or internal delamination is much smaller than that of solid materials [2], amplitude of
⁎
Corresponding author. E-mail address: [email protected] (H. Mitsuta).
https://doi.org/10.1016/j.microrel.2018.12.002 Received 6 September 2018; Accepted 2 December 2018 Available online 13 December 2018 0026-2714/ © 2018 Elsevier Ltd. All rights reserved.
the reflected signal in defects area is increased. Therefore, it is possible to detect defect areas from the difference in brightness value in the ultrasonic images. Semiconductor parts are currently being stacked and miniaturized as semiconductor products are becoming more multifunctional, faster, larger in capacity, and smaller in size [3]. For this reason, detection sensitivity must be improved to detect smaller defects at each interface. The defect detection sensitivity is affected by a beam diameter corresponding to a spatial resolution and ultrasonic energy corresponding to detectable depth. The beam diameter of the focused ultrasonic beam (d) and ultrasonic energy of the received signal (E) are calculated in Eqs. (2) and (3) [4,5].
d=a
vF fD
E = E0 exp(−2bf 2x )
(2) (3)
Here, a and b are constants, λ is a wavelength at peak frequency of the ultrasonic wave, F is a focal length of an acoustic lens, D is a diameter of the ultrasonic probe, E0 is initial ultrasonic energy, and x is a propagation distance of the ultrasonic beam. As shown in Eq. (2), the spatial resolution can be increased by applying the ultrasonic probe with higher frequency. As an example of this, gigahertz (GHz) SAM is used to detect defects several micrometers near the surface with the spatial resolution in 1 μm. On the other hand, as shown in Eq. (3), as the frequency of the ultrasonic probe increases, ultrasonic energy
Microelectronics Reliability 92 (2019) 172–178
H. Mitsuta, K. Sakai
Fig. 1. Principle of generating ultrasonic image using SAM.
increases only at the reflection point and becomes small in other points, so that it is robust to noise and a high signal-to-noise ratio (SNR). In the pulse compression method, the transmitted signal to be irradiated to the specimen greatly affects the pulse compression ratio. Therefore, we examined a modulation method effective for effectively improving the detection sensitivity and designed an optimum waveform. The conventional modulation methods are summarized in Fig. 3. First, the chirp modulation method is generated by continuously increasing or decreasing the frequency with time, which has an advantage that the waveform can be designed with two parameters: a frequency bandwidth (Δf) and a signal length (Δt). Here, the piezoelectric element used for converting the voltage signal into the ultrasonic signal has non-uniform frequency characteristics, so the waveform of the output ultrasonic signal is distorted and the designed voltage waveform cannot be output. When pulse compression is performed with the distorted waveform, the pulse compression ratio becomes lower than the ideal waveform, making it difficult to detect the reflected signal. Since the chirp modulation method that continuously changes the frequency needs to use a frequency band with high and low gain, the distortion owing to frequency characteristics is large, and waveform distortion is difficult to compensate for because there are few parameters. Next, the phase shift keying (PSK) method assigns signals of different phases to a given code string [7]. The advantage of this method is it can select frequencies with high gain since it can create the waveform with a single frequency, so the waveform distortion owing to frequency characteristics is low. However, since high frequency noise is generated at a phase switching point (arrows in Fig. 3), the designed signal is difficult to output, the same as in chirp modulation. Next, the frequency shift keying (FSK) method assigns signals of different frequencies to a given code string [7]. This method has the advantage that it can design waveforms at a few frequencies that have high gain, but the waveform takes much time to optimize because there are many parameters. However, FSK has larger technical advantages than other methods, so we decided to use FSK as the modulation method for pulse compression.
attenuates exponentially, and the detectable depth becomes shallow. Because of that, the spatial resolution and detectable depth are in a trade-off relationship, so minute defects in a deep part are difficult to detect by conventional SAM using pulse waves. In this research, we developed a technique that detects the attenuated ultrasonic signal with high sensitivity by signal processing and improves defect detection sensitivity in deep parts of the specimen. 2. Detection sensitivity improvement technique using pulse compression method To improve defect detection sensitivity, we applied a pulse compression method used to improve distance resolution in radar signal processing [6]. The pulse compression method is a signal processing technique that computes a correlation function between a signal transmitted to the specimen and a signal received at the ultrasonic probe. The correlation function is calculated as follows.
C (lT ) =
1 N
N
∑ u (nT ) v ((n + l) T ), n=1
(l = 0, 1, …, L − 1) (4)
Here, T is the sampling time interval, u(nT) is the received signal and v (nT) is the transmitted signal. A schematic image of pulse compression is shown in Fig. 2. In the pulse compression method, a modulation signal such as a pseudo-random number is used as the transmitted signal (Fig. 2(a)), and the received signal reflected from each interface (S0, S1, and S2 in Fig. 1) is acquired by the ultrasonic probe (Fig. 2(b)). When the modulation signal is transmitted to the specimen shown in Fig. 1, the received signal including three reflection waves can be acquired, but no reflected point can be distinguished in the received signal shown in Fig. 2(b). Here, when the correlation function is calculated between the transmitted and received signals, each reflected point is visualized (Fig. 2(c)) because the correlation function takes only a high value when the same waveform component with the transmitted signal exists in the received signal. In this way, the pulse compression method has the advantage that the correlation function
Fig. 2. Schematic images of pulse compression method. 173
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H. Mitsuta, K. Sakai
Fig. 3. Comparison of modulation methods.
3. Experiments The specimen was formed by stacking three layers of a 60-μm-thick silicon (Si) wafer and a 20-μm-thick polyimide film on a 400-μm-thick Si wafer and processing holes as the artificial defects in the polyimide film of the third layer from the top. The defects were square holes with sizes of 100 (2 lines), 50 (3 lines), 40 (3 lines), 30 (4 lines), 20 (5 lines), 10 (5 lines), 8 (10 lines), 6 (10 lines), 4 (15 lines), 2 (30 lines), and 1 (30 lines) μm as shown in Fig. 5. The specimen was ultrasonically inspected by using an ultrasonic probe having a nominal frequency of 200 MHz. The waveform of FSK was designed using sinusoidal waves with 100 and 200 MHz, and parameters of FSK were optimized by the method described in Fig. 4. The ultrasonic image was acquired by using a C-scan method [8] for scanning the ultrasonic probe two-dimensionally in the in-plane direction of the specimen. The scanning interval of the two-dimensional scan was set to 2 μm, and the observation area was 720 μm × 2800 μm. The conventional detection technique of ultrasonic signal using pulse waves (hereinafter, referred to as pulse waves) obtained the ultrasonic image by converting the amplitude of the reflection signal from the interface to be imaged into a brightness value. The proposed detection technique of ultrasonic signal using the pulse compression method (hereinafter, referred to as pulse compression) obtained the correlation function by calculating the transmission signal for the reflected signal from each interface in the specimen and obtained the ultrasonic image by converting the correlation function into the brightness value. To evaluate the difference in defect detection sensitivity between the conventional and proposed detection techniques of ultrasonic signal, ultrasonic images were acquired by using pulse waves and pulse compression.
Fig. 4. Optimization process of FSK waveform.
As discussed previously, to achieve high defect detection sensitivity, the transmission waveform must be suitably designed. The waveform design concept is shown in Fig. 4. First, the FSK waveform is created that combines multiple sinusoidal waves by inputting initial parameters. Next, the distorted waveform calculated by convoluting the frequency characteristic of the piezoelectric element to the designed waveform is created. Then, the autocorrelation function of the distorted waveform is calculated and the pulse width is obtained from full width at half maximum of the correlation function. By using the calculated pulse width as an evaluation function, parameters of FSK were optimized so that the evaluation function would be minimized by using the simulated annealing method. We evaluated the defect detection sensitivity by applying the optimized waveform to the detection of ultrasonic signal of a multilayer stacked specimen.
4. Results and discussion Fig. 6 shows ultrasonic images with defects ranging from 6 to 100 μm in the third layer bonding interface obtained by using pulse waves and pulse compression. In the ultrasonic image obtained by using pulse waves (Fig. 6(a)), 100 μm defects were observed, but smaller defects were difficult to detect, which is considered to be caused by the variation in brightness value, that is, large noise. In the ultrasonic image obtained by using pulse compression (Fig. 6(b)), the boundaries of the 100 μm defects were clearer than in the image obtained by using pulse waves, and defects smaller than 100 μm could be observed. Fig. 7 shows cross sectional profiles of defects ranging from 100 to 174
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H. Mitsuta, K. Sakai
Fig. 5. Schematic image of specimen.
20 μm. When pulse compression was used, the variation of the brightness value was smaller than when pulse waves were used and the brightness value of the defect parts was actualized. This result indicates that pulse compression reduces noise and improves visibility of the ultrasonic image. This is considered to be due to the fact that the SNR was improved by pulse compression, so the reflected signal from the specimen interface could be detected with high sensitivity. For quantitative evaluation, the defect detection sensitively was evaluated by comparing the standard deviation (σ) of the brightness value in the bonded area and SNR in each defect area of the ultrasonic image. Here, signal values (S) of SNR are defined as the brightness value in each defect part, and noise values (N) are defined as 3σ. Fig. 8 shows A to B cross-sectional profiles of the bonded region. As a result of comparing the cross-sectional profiles, the image obtained by using pulse waves was noisy, and the standard deviation 3σ from the extracted cross sectional profile was 0.03 for pulse waves and 0.01 for pulse compression. Based on these results, pulse compression reduced the noise value by 66%. Fig. 9 shows the relationship between defect size and SNR calculated by ultrasonic images shown in Fig. 6. Pulse compression was found to improve both SNR and the visibility of the ultrasonic image. For the 100 μm defects, SNR was 12.2 for pulse compression and 2.3 for pulse waves. To compare the defect detection sensitivity, ultrasonic images of an unsigned 8-bit integer were binarized. The threshold of binarization was determined under two conditions: a value at which false reports are minimized and a value at which the smallest defects can be detected. Fig. 10 shows the ultrasonic image binarized under the condition at which false reports are minimized (false report rate in the bonded area is less than 1%). Under this condition, pixels judged to be defects in the bonded area (inside the dashed-line box in Fig. 10(a)) were defined as false reports. When pulse waves were used, it was possible to recognize parts of 100 μm and 50 μm defects in the image binarized with the threshold value 187 (red circles in Fig. 10(a)). When pulse compression was used, 100 μm square defects could clearly be recognized in the image binarized with a threshold value of 136, unlike when pulse waves were used (Fig. 10(b)). Furthermore, 100% of 100, 50, 40, and 30 μm defects and 90% of 20 μm detects could be detected. Since the ultrasonic image obtained by using pulse waves
Fig. 6. Ultrasonic images obtained using pulse waves and pulse compression.
175
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Fig. 7. Cross-sectional profiles in defect areas. Left and right graphs show the results of using pulse wave and pulse compression, respectively.
noise could be reduced by calculating the correlation function, it is considered that the signal of the defect part could be revealed and even smaller defects could be detected. Next, to determine the minimum size of a detectable defect, the threshold of each ultrasonic image was adjusted, and the size of detected defects and the false report rate were evaluated (Fig. 11). The
contained larger noise than that obtained by using pulse compression as shown in Fig. 7, it seems that the threshold needed to be raised to suppress false reports. As a result of raising the threshold when pulse waves were used, the brightness value of the defect part was also buried, and it is considered that most regions containing a 100 μm defect could not be detected. When pulse compression was used, since 176
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Fig. 8. Cross-sectional profiles of bonded region.
Fig. 9. Comparison between signal-to-noise ratios of ultrasonic inspection methods.
binarized image obtained by using pulse waves was able to reveal two 50 μm defects (Fig. 11(a)). At this condition, the threshold was 165, and the false repot rate was 10.1%. By decreasing the threshold value below that in Fig. 10(a), the area where the defects were recognized expanded in the image obtained by using pulse waves (Fig. 11(a)), but the detectable defect size did not change and the noise increased. In the image obtained by using pulse waves, the signal reflected from the interface was buried in noise, and it seems that the difference between the bonded and the defect areas could not be detected. In the image obtained by using pulse compression (Fig. 11(b)), detection sensitivity improved and 10 μm defects were able to detected, where the threshold was 125 and the false repot rate was 9.8%. Some defects of 8 μm or less could be detected, but they could not be recognized as isolated detects. Because the beam diameter widened due to refraction and diffusion of the ultrasonic beam and the spatial resolution was reduced, it is considered that the signals reflected from the bonded region and the defect did not separate. To investigate the detection limit, it is necessary to evaluate defect detection sensitivity using an isolated defect and perform ultrasonic propagation simulation considering attenuation and beam spread in the material. Fig. 10. Binary images acquired under condition at which false reports are minimized. (For interpretation of the references to colour in this figure, the reader is referred to the web version of this article.)
5. Conclusion We developed a high sensitivity detection technique of ultrasonic signal using the pulse compression method for improving defect detection sensitivity. We examined a modulation method and optimized the waveform of the transmitted signal to achieve a high pulse compression ratio. The ultrasonic inspection was performed using pulse wave and pulse compression methods for a specimen formed by stacking three layers of a 60-μm-thick Si wafer and a 20-μm-thick
polyimide film and processing holes as the artificial defects in the polyimide film of the third layer. The defect detection sensitivity was evaluated by using a standard deviation and signal-to-noise ratio (SNR). The ultrasonic inspection technique using the pulse compression method reduced the noise value by 66% and increased the SNR 177
Microelectronics Reliability 92 (2019) 172–178
H. Mitsuta, K. Sakai
[2] L.W. Schmerr, Fundamentals of ultrasonic nondestructive evaluation – a modeling approach, Springer Series in Measurement Science and Technology, 2016. [3] Yole Development, Status of advanced substrates 2018: embedded dies & interconnects, substrate like PCB trends, Market & Technology Report, 2018. [4] Josef Krautkrämer, Herbert Krautkrämer, Ultrasonic Testing of Materials, SpringerVerlag, Berlin Heidelberg, 1983. [5] Z. Remili, Y. Ousten, B. Levrier, E. Suhir, L. Bechou, Scanning acoustic microscopy and shear wave imaging mode performances for failure detection in high-density microassembling technologies, 2015 Electronic Components & Technology Conference, 2015, pp. 2090–2101. [6] Thin Thin Mar, Su Su Yi Mon, Pulse compression method for radar signal processing, Int. J. Sci. Eng. Appl. 3 (2) (2014). [7] R.L. Brewster, W.W.S. Jibrail, Detection of FSK and DPSK data signals by pulse compression, Institution of Electrical Engineers Proceedings, vol. 129, 1982, pp. 273–280. [8] Lili Ma, Shengxiang Bao, Dechun Lv, Zhibo Du, Shilan Li, Application of C-mode scanning acoustic microscopy in packaging, electronic packaging technology, ICEPT 2007, 2007, pp. 1–6.
Fig. 11. Binary images acquired under the condition at which smallest defects can be detected.
compared with the ultrasonic inspection technique using pulse waves. Under a condition of false reports under 1%, 50 μm defects could be detected in images obtained by using the ultrasonic inspection technique using pulse waves whereas 20 μm defects could be detected in images obtained by using the ultrasonic inspection technique using the pulse compression method. This proved that the visibility of defects was improved in the ultrasonic image to which the ultrasonic inspection technique using the pulse compression method was applied. References [1] J.A. Khan, M. Mina, L. Udpa, S.S. Udpa, Analysis of scanning acoustic microscopy images of IC chips, Review of Progress in Quantitative Nondestructive Evaluation, vol. 14, 1995, pp. 915–922.
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