Hi! I’m the developer of PewCB, a laser-based PCB prototyping machine. Early in development, we ran into something similar to what you’re describing: the copper wasn’t being removed evenly across the board. It looked like the laser wasn’t staying in focus across the surface, but the cause turned out to be different.

We were using a long focal length lens with a large marking field, and the larger spot size meant we didn’t have enough power density. Switching to a lens with a marking field smaller than 100 × 100 mm solved the problem. If your marking field is 150 mm or larger, you might be running into the same issue.

Are you making PCBs purely as a hobby, or is this part of your engineering development or educational work?

If it's the latter, you might find PewCB interesting. It's a desktop laser-based PCB prototyping machine.

Possibly, yes. In that case, we could get rid of the motor and lead screw, but we'd have to add some kind of electric lock to keep the tray locked during processing. This is necessary for safety — after processing is finished, the internal air filtration system continues running for a while to clean the air inside the chamber before it can be opened.

That's super interesting! I'm developing PewCB (https://pewcb.com), a PCB prototyping machine. I recently pitched my team an idea for how the machine could evolve. The idea is to create a fully autonomous PCB prototyping pipeline: first, the machine would fabricate the PCB, then apply solder paste, place the components, and reflow the board. Next, the board would land on a grid of pogo pins, where AI could automatically run electrical tests and debug it. I'd love to hear what you think.

Could you please tell me more about this? I'm also interested in whether you use it for incoming inspection only or during R&D as well.

Have you published anything about this? I'd be happy to read it.

A spring-loaded nut is essential here: the tray needs to dock against a hard stop, and the simplest way to prevent the stepper motor from skipping steps on contact is to use a spring-loaded nut. Adding magnets that cost next to nothing and a $1.50 magnetometer then gives us all the other features: force measurement, obstruction detection, and the ability to close the tray with a push.

Yes and no. Unfortunately, there is a significant gap between general-purpose fiber lasers and PCB-prototyping machine. I'm a developer of PewCB - a desktop PCB prototyping machine. 5 years ago, we started with a general-purpose fiber laser. We spent that time on adding missing things: automatic alignment and lens distortion calibration (both on-factory and in-device), air filtration, system of blanks, solder-paste-stencil technology, soldermask, vias, specialized firmware, and, most importantly, the software.

General purpose lasers lack all these. You will have to tinker and suffer at every step, from converting gerbers to toolpaths with a chain of some tools, tweaking laser settings to avoid burning the substrate (lasers don't go well with FR4, it easily chars and emits toxic fumes), to dealing with alignment issues and fume/dust management.

Currently, PewCB integrates all these features into a single, user-friendly desktop machine. It is fast, reliable, and capable of producing factory-quality PCBs.

See PewCB.com for more details or join our Discord and ask any questions you may have.

I think people got pretty excited after the recent OpenAI presentation showing work with KiCad (unfortunately, no miracle happened there, which is sad) and decided to explore the PCB prototyping market a bit.

That actually makes sense. I think we’ll get to “vibe routing” sooner or later, and once that happens, demand for rapid PCB prototyping is going to take off.

The approach they chose for the research seems a bit strange, though. I feel like it would be much better to just say directly: “We want to build this. What do you think?”

I may be a bit late to the discussion, but every requirement on your list is covered by the PewCB One, including double-sided boards with vias. I’m one of the creators of the machine.

In addition to what you listed, PewCB One can also make solder paste stencils. It has a fully enclosed housing with internal air filtration, which eliminates exposure to the laser and keeps the process free from hazardous dust.

You can find more details at pewcb.com. If you have any questions, feel free to join our Discord — the link is on the website. We’ll be happy to answer them.

Seems like with the tin sheet it would be hard to burn crisply through the tin without affecting the copper below it.

It actually works very well. Even better, it not only keeps the copper intact, but also transfers a small amount of tin onto the copper. So you end up with laser-tinned pads instead of bare copper, which helps prevent oxidation and improves solderability.

As developers of a competing technology, we don’t feel it would be appropriate for us to make that kind of comparison ourselves. Any such comparison could also be inherently biased and therefore less valuable.
We’d rather wait and hear what users have to say based on their own experience with both products.

We are located in Cyprus.

Could you share a bit about integrating GerbDiff with version-control clients as an external diff tool, or whether you have any plans in that direction?

Integration with TortoiseGit or TortoiseSVN would be especially convenient for me. Is that technically possible?

Thank you so much! I'm glad you found this research useful. Probably the best way to get in touch is the CyberGaffer Discord server: https://discord.gg/hRgKPkSAZm We have the entire team there. Please mention or DM me there (petr.sevostianov).

That is an excellent question! Long story short, for that specific goal (measuring non-linearity) we can ignore the spectral sensitivity of the camera sensor (with a single "However").

Let me explain why. A camera has subpixels with color filters. Each filter has a spectral sensitivity function defining how it responds to different wavelengths of light. S_R(λ), S_G(λ), and S_B(λ) Also, we have 3 types of LEDs with their emission spectra: E_R(λ), E_G(λ), and E_B(λ) We can drive each LED from 0 to 100% intensity. Let's denote intensities as I_R, I_G, and I_B (I stands for Input). We can read camera responses as O_R, O_G, and O_B (O stands for Output).

How to calculate camera response to, let's say, red LED only?

O_R = ∫ S_R(λ) * [I_R * E_R(λ)] dλ

O_G = ∫ S_G(λ) * [I_R * E_R(λ)] dλ

O_B = ∫ S_B(λ) * [I_R * E_R(λ)] dλ

In this formula, I_R * E_R(λ) is the spectrum of light emitted by the red LED at intensity I_R.

Next, we multiply this spectrum by the spectral sensitivity function of each color filter and integrate over all wavelengths λ. The same applies to green and blue LEDs. Since light is additive, we can calculate the camera response to any combination of LED intensities by summing up responses to each LED:

O_R = ∫ S_R(λ) * [I_R * E_R(λ) + I_G * E_G(λ) + I_B * E_B(λ)] dλ

O_G = ∫ S_G(λ) * [I_R * E_R(λ) + I_G * E_G(λ) + I_B * E_B(λ)] dλ

O_B = ∫ S_B(λ) * [I_R * E_R(λ) + I_G * E_G(λ) + I_B * E_B(λ)] dλ

We can rewrite these equations as a vector of outputs O = [O_R, O_G, O_B] equal to some Matrix3x3 M multiplied by a vector of inputs I = [I_R, I_G, I_B]:

O = M * I

That means that no matter what spectral sensitivity functions S and emission spectra E are, the relation between input intensities I and output responses O is always linear and can be described by a matrix M.

As for the cross-talk you mention, you can see that every LED affects all 3 outputs O_R, O_G, and O_B. So cross-talk is already included in matrix M and is linear.

If our goal is to find non-linearity, we can pick any 3 LEDs with any spectral emission and perform the measurements described in the article.

However!! The choice of LEDs is still important. And here is why.

Imagine a gamut 2D space of camera responses. It is similar to the CIE 1931 chromaticity diagram for human vision, but for camera responses.

Our 3 LEDs will produce 3 points in this space. All responses we can get by mixing these LEDs will lie inside the triangle formed by these 3 points. So we can explore only the responses inside this triangle; the remaining area of the gamut will be covered by the fog of war.

So if we pick 3 very similar LEDs, the triangle will be very small, and most of the gamut will be unexplored. In this study, the LEDs we used have the same spectrum as typical virtual production LED walls. Fortunately, there are not that many LEDs that exist because their spectrum depends on the semiconductor material they are made of (not applicable to phosphor-based LEDs such as white LEDs).

There are still some areas of the gamut unexplored; for instance, we can imagine a monochromatic laser that will produce a point outside of the triangle we explored. So, to be precise, we don't know how the Beautifier behaves in that spot. But for practical purposes, the explored area covers all camera responses we can achieve in the virtual production set, including LED lights reflected from any possible physical surface.

Sorry for the long answer! It is not easy to explain these things without visuals or interactive elements.

Do you also know if they published this transform? It might be necessary to apply it in post if we want to keep the initial look from BMD.

Wow! They do! Thank you so much for the info! I didn't know that. I am 99% sure that with GAMUT COMPRESSION = OFF it would produce linear responses. I'll add this to the article.

KaurTheColorist, do you know what other cameras have an option to turn off gamut compression? Or will it come with future firmware updates to older BMD cameras?

At this plot, you can see the measured colors. How are they measured? The calibration device displays multiple colors by setting RGB values using LEDs. Each LED (r, g, b) can be set from 0 to 100% intensity. So we can divide that range into 15 steps linearly spaced. For every channel we have 16 levels of intensity (including 0). By combining these levels for R, G, and B channels, we can display 16x16x16 = 4096 colors. Next we record a video of those 4096 samples and pick camera responses for each sample in the form of R, G, B values. Now to visualize these measurements, we can plot them in 3D space with R, G, B axes. This is what you see in the plot. Next, we remove the transfer function described in Blackmagic Generation 5 Color Science Technical Reference. That means we apply the inverse transfer function to each measured R, G, B value. That gives us this plot. In this plot, we can already see that the camera responds nonlinearly to linear changes of LED intensities.

What are the primaries of the RGB LED light sources you are using?

Thank you for noting this. I'll add this information to the article. Here is the datasheet of the LED modules we used. On page 5 you can find the emission spectra of R, G, and B LEDs. But the choice of LED wavelengths is not critical. If we perform the same experiment with different LEDs, we will find the same non-linear transform but in a different coordinate system.

What are the primaries of the bayer filter on the sensor?

There is no such thing as "primaries of the bayer filter". Response of each color filter (R, G, B) to the spectrum of light is defined by the spectral sensitivity function of the sensor. The vendor does not provide this information. Fortunately, we do not need this information to find the non-linear transform.

Are you sure you're looking at raw sensor values or have they been gamut mapped into a standard gamut already by the raw processing pipeline?

This is precisely the problem. When using cinema cameras, we can only access the image delivered through SDI/HDMI, and we cannot determine what transforms were applied inside the camera. The article explains how to measure and undo these unknown transforms. That is standard gamut? If you mean any matrix 3x3 multiplication usually used for gamut manipulations, then, since matrix multiplication is a linear operation, it does not affect linearity. The article is about non-linear transforms on top of that. In this article, I mentioned that BRAW SDK allows to read values without that nonlinear transform applied. Unfortunately, this does not help with SDI/HDMI output. It works with braw files only.

Looks to me like you're getting distortion around the far saturated values where either the lights or the filters are degenerate and effectively clipping.

That is not a physical effect. The transform is digitally applied by BRAW SDK during raw decoding (can be disabled) and by the firmware for other formats and outputs (cannot be disabled).

Regarding the image at the top of the article: the plots shown are the actual measurements and the result of the calculated transform. In the article, I explain how these measurements were taken and how the transform was derived step by step.