Carving billions of transistors on a very small wafer is the daily routine of semiconductor manufacturing – but few people know that this “nanoscale production” is always struggling with “micron-level mistakes”. Once there is a very small scratch or a slight deviation of the wire from the designed trajectory, the entire batch of chips may become scrap.
Manual inspection is hard to achieve micro-level precision and high-frequency repetitive work-after all, no matter how sharp the eyes are, it cannot compete with the “24-hour continuous operation” of machinery. At this critical moment, machine vision stepped forward and became the core force in this “precision defense battle”. Today, let’s look at how this pair of “electronic eyes” works from the entire process of semiconductor manufacturing.
The two major difficulties in semiconductor manufacturing can only be solved by machine vision.
Why is machine vision an essential requirement for semiconductor manufacturing? The answer lies in two long-standing questions.
First, the “complexity” of the process. From the just-completed front-end processing of the wafer to the final “finishing touches” of packaging and testing, the entire process must pass through hundreds of checkpoints. Just the core front-end processes alone need to be repeated 40 to 100 times. Each repetition must ensure there are no issues at all-just like observing the structure of each granulocyte tissue under a microscope; even the slightest mistake won’t do.
Second, zero tolerance for defects. For semiconductors, “defects” are equivalent to “scrapping”: micro-sized particle contamination can cause short circuits in circuits, and even slight deviations in wires can lead to signal interruptions. If the problem is only discovered after mass production, the losses could amount to millions. Therefore, “identifying problems early to minimize losses” has become a very important point in production.
Machine vision, on the other hand, is precisely the key to solving these two predicaments: it can automatically complete detection, measurement, and alignment with “sub-pixel-level” precision (equivalent to dividing 1 pixel into dozens of parts). Not only are its speed dozens of times faster than manual labor, but the results are also not affected by fatigue or emotions. More importantly, even after being repeated thousands of times, the precision will not be compromised.
Look at the “main strengths”: The “Three Important Skills” of machine vision.
To gain a firm foothold in the semiconductor field, machine vision does rely on three sets of “hardcore skills”:
The first: “The ability to identify flaws.”
Relying on deep learning tools like MVTec HALCON, it can accurately identify tiny cracks and scratches on the surface of wafers in environments with chaotic lighting and complex backgrounds. Even if the defects are thinner than a human hair, they cannot escape its “eyes”, and it can automatically “circle out” the defect areas for convenient subsequent processing.
The second, “Observation at the micrometer level”.
Given it milliseconds of time, it can complete sub-pixel-level measurements of straight lines and arcs. When it comes to complex 3D structures like wafer bumps, it can also “replicate” the surface shape through 3D reconstruction technology, with the highest measurement accuracy reaching 1/50 pixel – equivalent to measuring one ten-thousandth of the diameter of a human hair.
The third, “Precise Navigation”.
In semiconductor manufacturing, “alignment” is crucial: layers must be aligned with each other, and the contact between probes and circuits must be aligned. Even if the wafer rotates, scales, or is partially obscured, the sub-pixel-level shape matching technology of machine vision can still find the “correct position” in real time, ensuring that every alignment is “zero deviation”.
Full-process attack, from the “front end” to the “end”, the “main map” of machine vision.
1.Front-end Foundry: Guarding the “First Line of defense” of wafers.
When wafers are just produced, the most feared thing is surface defects. Machine vision will first use a combination of “shape matching + deep learning” to carefully scan every inch of the wafer, identifying every tiny scratch and particle contamination – even if the lighting in the workshop is uneven, no errors will occur.
In addition, it also needs to keep a close eye on the “Redistribution Layer (RDL)” – which is an important component for connecting circuits within the chip. Machine vision will check whether the pattern of RDL is complete with sub-pixel precision to ensure that each “line” complies with the design specifications and avoid the risk of “open circuit” in the future.
2.Back-end Battlefield: Guarding the “Last Line of Defense” of Packaging.
In the packaging stage, the tasks of machine vision are more precise:
- Identity Recognition: Through deep OCR technology, the unique ID (such as DMC code, QR code) on the wafer or chip is read, just like giving each chip an “ID card”. The entire process from production to delivery can be traced, and it can also reduce the misjudgment of similar characters – such as distinguishing “0” from “O”.
- “3D Inspection”: For the metal solder balls (bumps) on the wafer, it uses 3D surface inspection technology to measure the height and diameter of the bumps, ensuring that these “connection points” can stably connect to the circuit board – even if the shape of the bumps is irregular, they can still be precisely positioned.
- “Flip-chip inspection”: For flip-chip chips without a casing, it will use point cloud processing algorithms to check the coplanarity of the bumps (whether they are in the same plane) and the horizontal section to ensure that the chip can be directly “seamlessly connected” to the circuit board.
- “Wire Control”: The wires in the chip are like “nerves”. Machine vision uses “Depth of Focus (DFF) technology” to confirm whether the 3D trajectory of the wires is correct, and at the same time check the “bonding points” between the wires and the chip – whether there are gaps and if the positions are correct, to avoid signal transmission problems.
3.The main part of the test: “Precise navigation” of probe contact.
Probe testing is the “final physical examination” of semiconductors just before they are completed: the probe must precisely contact the circuits on the wafer. A slight mistake could damage the wafer or lead to inaccurate test results. At this point, machine vision will correct the tilt of the wafer by adjusting the “focal plane”. By using shape-matching technology, the errors caused by wafer rotation are eliminated, ensuring that the probe can “precisely touch the target” every time.
Finally: From “Precision Guardian” to “Industry Promoter”.
Nowadays, chips are getting smaller, and their performance is getting stronger. The requirement for precision has moved from the “micron-level” to the “nanoscale level”. Machine vision is also upgrading – with higher precision, faster speed, and the ability to handle more complex scenarios.
It is no longer merely a “quality inspector”: by reducing the defect rate, it can help enterprises improve the “yield rate” (the proportion of qualified products). By increasing the detection speed, it can drive the production line to “improve efficiency”. It can be said that in the future of semiconductor manufacturing, every process cannot do without this pair of “tireless electronic eyes” – they not only safeguard “precision” but also drive the entire semiconductor industry forward.
Source: Photosynthetic Vision Inspection Expert
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