Case Report
Evaluating Gold Wire Bonding Pull Strengths on Different Thicknesses of Reduction-Assisted Immersion Gold
Issue:
Volume 12, Issue 1, June 2025
Pages:
1-7
Received:
5 May 2025
Accepted:
19 May 2025
Published:
19 June 2025
Abstract: Wire bonding attaches a fine wire from one connection pad to another, completing an electrical connection. Reduction-assisted immersion gold (RAIG) has recently gained popularity due to its ability to eliminate corrosion and plate thicker gold deposits. Implementing a RAIG process improves quality and reduces wire bonding product reliability risk. The purpose of this research was two-fold: 1) evaluate the effects of crossing two RAIG thicknesses against three wire bonding gram-forces on wire bond pull strength, and 2) evaluate two independent RAIG thickness effects on wire bond pull strength, all on an electroless nickel electroless palladium immersion gold (ENEPIG) final finished printed circuit board. A quantitative, experimental research methodology was used to manipulate independent variables to observe the effect on the dependent variable, establishing cause-and-effect relationships for wire bonding. This method was selected because of its ability to identify and quantify statistically significant factors for gold plating and wire bonding. Data was generated and collected in a controlled laboratory setting. Multi-variate charts, analysis of variance (ANOVA), lognormal distributions, and descriptive statistics were used for data analysis. As the RAIG deposit thickness increases, the bond gram-force is not vital for wire pull strength. Thicker RAIG deposits statistically outperform thinner RAIG deposits for wire pull strength. A mixed reaction RAIG electrolyte enables robust designs and achieves world-class quality "on target with minimal variation."
Abstract: Wire bonding attaches a fine wire from one connection pad to another, completing an electrical connection. Reduction-assisted immersion gold (RAIG) has recently gained popularity due to its ability to eliminate corrosion and plate thicker gold deposits. Implementing a RAIG process improves quality and reduces wire bonding product reliability risk. ...
Show More
Research Article
Image Reconstruction in Compressive Sensing Using the Level 3 Reverse Biorthogonal 4.4 (rbio4.4) Discrete Wavelet Transform and SP, CoSaMP and ALISTA Algorithm
Issue:
Volume 12, Issue 1, June 2025
Pages:
8-15
Received:
1 October 2025
Accepted:
14 October 2025
Published:
31 October 2025
Abstract: This paper presents an efficient image reconstruction method based on Compressive Sensing (CS) theory, leveraging the level-3 Reverse Biorthogonal 4.4 (rbio4.4) discrete wavelet transform in combination with three reconstruction algorithms: Subspace Pursuit (SP), Compressive Sampling Matched Pursuit (CoSaMP), and the Analytic Learned Iterative Shrinkage Thresholding Algorithm (ALISTA). The approach exploits the sparsity of images in a suitable wavelet basis, enabling compressed acquisition from a reduced number of random linear measurements. The process consists of four stages: (1) decomposition of the original image using the rbio4.4 wavelet transform to obtain sparse coefficients, (2) compressed sampling via a random measurement matrix, (3) reconstruction of the sparse signal using SP, CoSaMP, or ALISTA, and (4) final image reconstruction through the inverse wavelet transform. Experimental evaluation was conducted on the classic Lena image (200 × 200 pixels), comparing the three algorithms in terms of reconstruction quality measured by the Structural Similarity Index (SSIM) and computational cost (reconstruction time in minutes) across sampling rates ranging from 10% to 60%. Results show that all three algorithms achieve nearly identical reconstruction quality (virtually indistinguishable SSIM values at each sampling rate), confirming their effectiveness within the CS. However, ALISTA stands out significantly due to its exceptional speed, exhibiting substantially lower reconstruction times thanks to its learned nature, which replaces iterative procedures with fixed, optimized operations. In contrast, CoSaMP demonstrates higher and sometimes unpredictable computational times depending on the sampling rate. These findings highlight ALISTA’s strong potential for real-time or embedded applications.
Abstract: This paper presents an efficient image reconstruction method based on Compressive Sensing (CS) theory, leveraging the level-3 Reverse Biorthogonal 4.4 (rbio4.4) discrete wavelet transform in combination with three reconstruction algorithms: Subspace Pursuit (SP), Compressive Sampling Matched Pursuit (CoSaMP), and the Analytic Learned Iterative Shri...
Show More
,
Marie Emile Randrianandrasana
