The semiconductor industry is characterized by its relentless pursuit of miniaturization and precision, resulting in increasingly complex manufacturing processes. At the heart of this industry is the fabrication of integrated circuits (ICs) on silicon wafers—a process that demands the highest levels of accuracy and consistency. The silicon wafer, a thin slice of semiconductor material, serves as the substrate on which microelectronic devices are built. As these devices continue to scale down, the inspection and analysis of wafer lot become even more critical to ensure high yield and performance. It explores the growing influence of machine vision in semiconductor production, particularly focusing on its use in wafer inspection and analysis.

One technology that is revolutionizing the wafer inspection process and playing a pivotal role in semiconductor manufacturing is machine vision. Machine vision refers to the use of a combination of hardware and software to provide imaging-based automatic inspection, process control, and robot guidance. It replaces or complements manual inspections and measurements with digital cameras and image processing. This technology brings about a level of speed, consistency, and precision in inspection tasks that is unparalleled by human operators.

I. Challenges in Conventional IC Packaging and Die Counting

Integrated Circuit (IC) packaging is a critical step in the manufacturing of semiconductor products. This process encapsulates and protects the IC, providing physical and electrical connections to the device it’s serving (Wong et al., 2006).

Conventionally, each wafer of a given size and processing parameters yields a fixed number of dies. However, the actual number of packaged dies can be significantly reduced due to factors such as physical damage (scratching, contamination, or breakage), thereby leading to a discrepancy between expected and actual yield (Reid et al., 2008).

Current die counting methods often overlook these irregularities, resulting in inaccurate yield predictions. This discrepancy can cause significant logistical and economic challenges in the semiconductor manufacturing process (Hansen et al., 2011).

II. Machine Vision-Based Counting Technique: An Innovative Solution

Machine vision, a technology that enables automatic inspection and analysis, has been widely applied in various industrial sectors, including semiconductor manufacturing (Zhang & Lu, 2017).

Machine Vision-Based Counting Technique
There can be more machine vision based counting techniques, but some useful techniques for wafer die counting are given below.

Wafer map software

Wafer map software plays a pivotal role in semiconductor manufacturing, serving as an invaluable tool for the visualization, analysis, and categorization of defects on silicon wafers. With the capabilities to accurately identify imperfections, it fosters enhanced quality control and informed decision-making in processes such as die sorting and packaging

Wafer Region Detection:

The method first identifies the regions on the wafer where the dies are located, using image processing techniques (Lu & Tsai, 2018).

Wafer Position Calibration:

The position of the wafer is then calibrated to ensure accurate detection and counting of dies. This step aligns the image with a predefined coordinate system for consistency across different wafers (Zhang & Lu, 2017).

Dies Region Detection:

This step uses advanced image processing techniques like edge detection and pattern recognition to detect the dies, even those that are broken or fragmented (Kumar & Zhou, 2019).

Detection of Die Sawing Lines:

The method identifies sawing lines – marks left by the saw that cuts the wafer into individual dies – through edge detection algorithms (Lu & Tsai, 2018).

Die Number Counting:

Finally, the number of dies is counted based on the detected regions and sawing lines through die per wafer calculator. This includes both whole and fractional dies remaining on the wafer boundary (Kumar & Zhou, 2019).

Experimental results of this approach have demonstrated high precision and recall rates of 99.83% and 99.84% respectively, indicating its reliability and accuracy (Zhang & Lu, 2017).

III. Importance and Benefits of Machine Vision in Semiconductor Manufacturing

Machine vision has found widespread applications in semiconductor manufacturing, including automatic optical inspection, wafer defect detection, and more (Lu & Tsai, 2018).

Accurate die counting is vital for semiconductor manufacturers as it affects yield estimation, inventory management, and operational efficiency (Hansen et al., 2011).

Despite the significance of accurate die counting, there are few studies focusing on automatic die counting that do not rely on manual input of size and dimension information for dies and wafers (Reid et al., 2008).

IV. Impact on Costs and Efficiency in IC Testing and Packaging

The semiconductor industry faces significant challenges due to the high costs of IC testing and packaging. These costs constitute a major part of the overall IC production cost (Wong et al., 2006).

The machine vision-based method offers a promising solution to this problem. By providing an efficient and accurate way to determine the number of successfully packaged dies, it could significantly reduce costs and time, improving productivity (Zhang & Lu, 2017).

The method can handle all cases of residual dies, including those on the wafer boundary and those that are broken, fractured, or dropped, thus further improving yield predictions (Kumar & Zhou, 2019).


By accurately accounting for anomalies such as fractional dies on the wafer boundary and dropped dies during packaging, the method can significantly improve the accuracy of die counting (Lu & Tsai, 2018).

Apart from increasing accuracy, this method eliminates the need for time-consuming and error-prone manual inspection, further enhancing its value to the semiconductor industry (Hansen et al., 2011).

In conclusion, the proposed machine vision-based die counting method offers a robust, accurate, and cost-effective solution to the challenges faced in semiconductor manufacturing. Further research and development in this area will continue to drive improvements in yield, cost-efficiency, and operational efficiency in the semiconductor industry (Zhang & Lu, 2017).


  • Hansen, P., & Qvist, P. (2011). Counting methods for manufactured products. Production Planning & Control, 22(4), 315-324.
  • Kumar, S., & Zhou, Z. (2019). Machine vision system for 3D metrology and inspection in semiconductor packaging. Sensors, 19(6), 1304.
  • Lu, C., & Tsai, D. (2018). A machine vision method for wafer quality inspection. International Journal of Advanced Manufacturing Technology, 96, 995-1005.
  • Reid, D., Zhou, Z., & Tian, G. (2008). Impact of defects on the yield and reliability of semiconductor devices. Microelectronics Reliability, 48(3), 497-504.
  • Wong, C. P., Moon, K. S., & Li, Y. (2006). Advanced Materials for High Density Electronic Packaging and Interconnects. Materials Science and Engineering, 50, 1-68.
  • Zhang, X., & Lu, J. (2017). Advances in machine vision applied to electronics and semiconductor manufacturing. Measurement, 110, 501-513.

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