How to send edge plating annotation diagram to JLCPCB?

Q: JLCPCB edge plating guideline says this - "supply an annotated diagram showing which edges to plate".

I have this edge plating annotation diagram ready. However, there is no place to actually include this diagram during the PCB order process in the JLCPCB website. So my question is, where to attach this edge plating annotation diagram?

On a side note, I also included detailed fabrication notes (board dimensions, stackup, controlled impedance, drill summary, etc.) with my PCB and exported this with the Gerbers for JLCPCB, in case they have any questions. However, they mentioned that they would discard all these fabrication notes in an email. How do y'all send fabrication notes to JLCPCB? Is it not required?

A:

1:Edge plating diagram

For edge plating, you can directly design the plating area in the Gerber file (normally on a separate layer). When placing the order, please also add a remark such as “Edge plating as per the dedicated layer in the Gerber”. In addition, we strongly recommend selecting the option “Confirm Production File” during checkout, so our engineers can confirm with you before fabrication.

2: Fabrication notes (dimensions, stackup, impedance, drilling, etc.)

Our engineers mainly follow the Gerber data for production. Other supplementary files in your compressed package (such as English notes, DXF, Excel, etc.) will not be referenced to avoid any conflict with the Gerber. Therefore, please ensure all required parameters are correctly defined in your Gerber files, as these will be used as the sole basis for manufacturing.

① Board dimensions: Just make sure the outline in the Gerber is correct. The website will also automatically recognize the size. If you find the detected size to be inaccurate, you can include the correct dimension in the remarks.

② Stackup: You can select the required stackup when placing the order. For impedance-related designs, you may refer to our impedance calculator to choose the most suitable stackup: https://jlcpcb.com/pcb-impedance-calculator.

③ Controlled impedance: By default, impedance tolerance is ±20%. If you require tighter control (±10%), please contact our online customer service or email our support team (support@jlcpcb.com) to enable the whitelist option. Once enabled, you can select ±10% tolerance. Please also include the impedance details (trace width/spacing, layer location, etc.) directly in the Gerber package and mention in the remarks where this information can be found.

④Drilling: Please design drills in the drill layer. Supplying a drill drawing file in the Gerber package is also recommended.

Overall, as long as your Gerber files are designed clearly, no extra documentation is needed. But for special requirements, remarks + confirm production file are the most effective way to ensure your instructions are followed. #JLCPCB#

PCB
DIY Current Meter using a Ferrite Core and Hall Sensor

I built a DC ammeter using a toroidal ferrite core and a Hall Effect sensor (WSH130NL). The idea is, when current flows through a wire around a toroidal core, it creates a magnetic field inside the core. For AC measurements we can just use a secondary winding and get a pretty good step down transformer producing an effective voltage which can be converted into current values. But with a DC steady signal we have to measure the magnetic flux in a different manner. Overall now with this approach we get an isolated current measurement. And by choosing the right number of windings, you can adjust the sensitivity and resolution of your ammeter to suit your needs. The same can be converted later with the help of a PCB provided by JLCPCB. As I got my MCU dev board fabricated from there in a $2 price. See the full article with code from here: https://www.hackster.io/sainisagar7294/diy-current-meter-using-a-ferrite-core-and-hall-sensor-4a91aa

8732326356871602176

How Does It Work?

When current passes through a conductor coiled around a toroidal ferrite core, it generates a magnetic field within the core. Due to the ferrite’s high permeability, the magnetic field is largely confined inside the core. By placing a Hall Effect sensor in the air gap, the sensor is positioned directly in the path of the magnetic flux. It then generates a voltage proportional to the magnetic field passing through it.

Components Required

WSH130NL Hall Effect Sensor

Toroidal Ferrite Core

Enamelled Copper Wire

10K Ohm Resistor

0.1uF Capacitor

Arduino Uno/Nano

16x2 LCD Display I2C

Building the DC Ammeter - Step by Step

The toroidal ferrite core is the heart of this ammeter. You can salvage one from an old power supply, a common-mode choke, or buy one online. The size doesn't matter too much, but a larger core gives you more room to wind wires.

Cutting the Air Gap:

This is the most critical and tricky step. We need to cut a small gap in the toroid where the Hall sensor will sit.

Winding the Toroid:

I used 10 turns of 24 AWG enamelled copper wire on a ferrite core. More windings mean a stronger magnetic field for the same current.

Placing the Hall Effect Sensor:

You can place the sensor, make sure it is flat in the middle of the gap, then secure it using hot glue and some tape. The orientation decides the polarity, but it doesn't matter even in this case because the steady state sensor readings are common mode by VDD/2.

Arduino Code:

The Arduino's 10-bit ADC gives us 1024 steps across 0-5V, which translates to approximately 4.88mV per step.

How to Calibrate:

  1. Upload the code to your Arduino with SENSITIVITY set to 0 initially
  2. With no current flowing, write down value of V_OFFSET.
  3. Pass a known current of say 1A and note the voltage.
  4. Pass another known current of 3A, note the voltage
  5. Update the V_OFFSET and SENSITIVITY defines in the code

Working:

At 0A:

8732326685126356992

At 1A:

8732326749445873664

At 2A:

8732326873350213632

At: 3A:

8732326927783485440

#HallEffect# #PCB# #currentmeter# #powermeter# #Arduino#

Test & Measurement
The Practicality of the Discrete Fourier Transform (DFT)

In typical applications of science and engineering, the Discrete Fourier Transform (DFT) stands as the most vital tool for digital signal processing. While other Fourier versions are essential for theoretical understanding, the DFT is uniquely suited for computer implementation. This is because both the time-domain sequence and its frequency-domain representation are discrete and finite. In my professional view, mastering the DFT is a mandatory requirement for any engineer working with digital systems and numerical algorithms.

The DFT allows you to analyze the frequency content of a signal with acceptable accuracy using finite computational resources. In my text, the analysis equation for a discrete sequence of length N is defined exactly as:

image.png

The practicality of this transform arises from the fact that it can be computed extremely fast using the Fast Fourier Transform (FFT) algorithm. This efficiency is what enables real-time spectral analysis in your drone communication links and radar systems. When you process a finite set of samples from an analog signal, you are essentially performing a DFT to understand its spectral characteristics.

By using the DFT, you can perform operations such as linear filtering and correlation in the frequency domain with much less arithmetic effort than in the time domain. This computational advantage is the primary reason why digital signal processing has revolutionized modern engineering. Mastering the properties and implementation of the DFT will provide you with a powerful analysis tool for your future projects in telecommunications and embedded systems.

image.png

The visualization shown comparison between a finite-length sequence and its corresponding discrete frequency spectrum.

Analog & Signal
The Four Versions of Fourier Analysis

In typical applications of science and engineering, we must choose the appropriate mathematical tool based on the characteristics of the signal we are processing. I believe that a clear understanding of the four versions of Fourier analysis is essential for your professional career in signal processing. These versions are distinguished primarily by whether the signal is continuous or discrete and whether it is periodic or aperiodic.

In my text, I classify these methods into four distinct categories. When you deal with continuous periodic signals, you utilize the Fourier Series (FS). If the signal is continuous and aperiodic, the Fourier Transform (FT) is the required tool. For your digital systems, we use the Discrete-Time Fourier Series (DTFS) for periodic sequences and the Discrete-Time Fourier Transform (DTFT) for aperiodic sequences.

The Discrete-Time Fourier Series is particularly important in digital engineering. The analysis equation for a discrete periodic signal with period N is defined exactly in my book as:

image.png

This equation allows you to determine the frequency components of a discrete signal with acceptable accuracy. It is crucial to remember that the properties of the signal in the time domain directly determine the properties of its representation in the frequency domain. For instance, periodicity in one domain always leads to discreteness in the other. Mastering this interplay will enable you to select the most efficient numerical algorithms for your specific engineering challenges.

image.png

these four versions based on time and frequency characteristics. (a) A periodic waveform; (b) its frequency-domain representation; (c) the frequency components of the waveform in (a); (d) the square error in approximating the waveform in (a) using only the dc component with different amplitudes.

image.png

(a)Samples at intervals of 1s, of a periodic continuous signal x(t) with period 5s;

(b)its scaled DFT spectrum;

(c)samples of x(t) at intervals of 0.125s;

(d)its scaled DFT spectrum.

The diagram illustrates the relationship between a periodic discrete signal and its corresponding frequency spectrum.

Analog & Signal
Impulse and Sinusoidal Signals

In typical applications of science and engineering, I believe that we must represent arbitrary signals in terms of simple and well-defined basic signals. The two most fundamental signals that you will encounter in your engineering career are the unit impulse and the sinusoidal signal. These signals serve as the building blocks for representing and processing more complex information.

The discrete unit impulse signal, which I denote as image.png, is the simplest signal in discrete signal analysis. It is defined precisely in my text as:

image.png

This signal is essential because any arbitrary signal can be represented as a sum of shifted and scaled impulses. This representation is known as the sifting property of the impulse. By understanding the response of a system to a single impulse, you can effectively determine its response to any practical signal you might encounter.

Similarly, sinusoidal signals are crucial for frequency domain analysis. A discrete sinusoid is expressed with the following mathematical formula:

image.png

In my study of linear time invariant systems, sinusoidal signals are preferred because they are eigenfunctions of these systems. When you apply a sinusoidal input to a system, the output is also a sinusoid of the same frequency. This unique property allows us to characterize a system entirely by its frequency response. Mastering these basic signals is a vital prerequisite for all advanced signal processing tasks.

image.png

The illustration shows the discrete unit-impulse and unit-step signals clearly. 3 (a)The unit-impulse signal δ(n); (b)the unit-step signal u(n);(c)the unit-ramp signal r(n)

image.png

The waveforms a discrete sinusoid and its corresponding even and odd components.

Analog & Signal
Time-Domain vs. Frequency-Domain Analysis: A Brief Overview

In typical applications of science and engineering, we have to process signals using systems. In my signals and systems course, I focus on presenting two primary methods of analysis that are essential for your professional career. Time-domain analysis involves studying the variation of a signal amplitude as a function of time. This approach is highly intuitive because most practical signals are continuous functions of time, such as the telemetry data from your drone flight controllers.

Conversely, frequency-domain analysis allows us to look inside the signal to understand its spectral composition. I believe that a good grounding in frequency-domain methods is required to specialize in communication and signal processing. The transition between these domains is governed by Fourier analysis. For a discrete sequence of length N, the forward transformation is represented exactly in my text as:

image.png

By transforming your signals, you can analyze the frequency response of a system with acceptable accuracy. The major advantage of this shift is that operations like convolution in the time domain are replaced by simple multiplication in the frequency domain. This makes complex analysis much easier when designing filters or analyzing signal integrity. Mastering the interplay between these two domains is essential for modern engineers to choose the most efficient analysis method for any given practical problem.

image.png

The comprehensive overview the different versions of Fourier analysis depending on whether the signal is continuous or discrete.

(a) A periodic waveform; (b) its frequency-domain representation; (c) the frequency components of the waveform in (a); (d) the square error in approximating the waveform in (a) using only the dc component with different amplitudes

image.png

This visual demonstrates a periodic discrete sinusoid alongside its discrete-frequency spectrum.


Analog & Signal
Understanding Convolution in LTI System Analysis

In the analysis of linear time-invariant (LTI) systems, the most fundamental task involves determining the response of a system to an arbitrary input signal. My methodology emphasizes that if we possess the impulse response of a system, the output for any input can be calculated through the process of convolution. This operation serves as the definitive cornerstone of time-domain analysis in engineering.

Decomposing an input signal into a sum of weighted and shifted impulses allows us to compute the total output by summing the individual responses. For a discrete-time LTI system, the input-output relationship is defined by the convolution sum. In my text, this essential relationship is expressed exactly as:

image.png

The convolution process involves four primary mathematical steps: folding, shifting, multiplying, and summing. As you implement these steps in your design, you will observe how the impulse response characterizes the physical behavior of your hardware or signal processing algorithm.

image.png

The illustration provides a clear visualization of these operations. This specific image demonstrates the folding and shifting of the impulse response sequence relative to the input sequence. By examining this diagram, the graphical interpretation of the sum of products becomes intuitive for any engineer designing digital filters or drone communication protocols. Mastery of this tool is a vital prerequisite before you move toward complex frequency-domain representations.

Analog & Signal
Why Small Edge Features Matter More Than Many CNC Part Designs Show

In CNC machining, most design discussions focus on dimensions, tolerances, hole positions, thread depth, and surface finish. Those are obviously important, but many assembly and handling problems begin somewhere much smaller. The edge condition of a part, especially around holes and contact features, often decides whether a machined component feels clean and well-made or awkward and troublesome in real use.

This is one reason small edge features deserve more attention during design review. A part can be dimensionally correct and still create unnecessary problems if the entrance to a hole is too sharp, if burrs remain around a cut feature, or if repeated assembly quickly damages an untreated edge. These issues are easy to dismiss during CAD work because they do not look dramatic on the model. In production and assembly, they show up immediately.

A sharp hole entrance is a good example. On paper, the hole diameter may be perfect. In practice, a screw may start poorly, a pin may catch at the edge, or an operator may need to force the part into position. Once that happens, the edge is more likely to chip, deform, or raise a burr. The result is not just an ugly entrance. It becomes an assembly problem, and people often blame the fit or the tolerance before they look at the edge itself.

This is especially common on threaded holes, locating holes, and repeated-use features. A threaded hole may technically be correct, but if the entrance condition is rough or too sharp, fastener starting becomes less consistent. A locating pin may fit once but damage the hole mouth after repeated installation. On parts that are assembled and disassembled many times, the edge condition becomes part of the product’s real durability.

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That is why controlled edge preparation matters. In many CNC parts, a very small edge treatment can reduce a much larger downstream problem. It may help guide entry, protect the feature during handling, reduce burr-related interference, and make the part feel more deliberate in assembly. This is not just about appearance. It is about how the part behaves.

The same principle applies to outer edges. A part does not need razor-sharp corners to be considered precise. In fact, sharp untreated corners are often less practical. They chip more easily, feel rougher in handling, and may create avoidable safety concerns during assembly or inspection. A controlled break at the edge often improves the part without affecting the design intent in any negative way.

Of course, not every edge needs the same treatment. Some edges only need a light break to remove sharpness. Others are functionally important and need a more deliberate feature to support alignment or entry. This is where machining knowledge becomes valuable. If every edge is treated casually, important features may remain under-controlled. If every edge is over-specified, cost and machining time go up without adding much value.

For designers and buyers, the practical question is not whether every part needs aggressive edge treatment. The real question is which edges affect function and which ones simply need to stop being sharp. Hole entrances, thread starts, locating features, and repeated assembly areas usually deserve more attention than random exterior corners.

This is also where communication on the drawing matters. General notes such as “break sharp edges” can be useful, but they are not always enough when the edge geometry affects assembly. A controlled entry feature should be treated differently from a casual deburred corner. If the edge is doing real work, the print should make that clear.

A good example of this thinking can be seen in the way chamfers are used on machined parts. A chamfer may look like a very small detail, but it often improves entry, protects edges, and reduces assembly frustration in ways that are easy to underestimate. A more detailed explanation of that topic can be found in this guide on chamfer in CNC machining: purpose, angles, applications, and drawing guide.

The broader lesson is simple. Small edge features are often treated like secondary details, but many of them influence how a part is actually used. In CNC machining, the difference between a part that merely passes inspection and a part that assembles cleanly, handles well, and lasts longer is sometimes found in those details.

When edge condition is reviewed early, many downstream problems become easier to prevent. Burrs are easier to control. Hole entry becomes smoother. Threads start more cleanly. Handling damage is reduced. Repeated assembly becomes more consistent. None of that changes the headline dimensions of the part, but it changes the real quality of the result.

For that reason, edge treatment should not be treated as an afterthought. It should be considered as part of how the design functions in production and in use. Small features often carry more value than they appear to on the drawing.

Design & Manufacturing
The Transition from Continuous to Digital Signal Processing

For most practical systems, input and output signals are continuous, and these signals can be processed using continuous systems. However, due to rapid advances in digital systems technology and fast numerical algorithms, it is highly advantageous to process continuous signals using digital systems by converting the input signal into a digital signal.

In my text, I always emphasize that a discrete signal is specified only at discrete values of its independent variable. For example, a continuous signal x(t) is represented only at t=nTs as x(nTs), where Ts is the sampling interval and n is an integer. The discrete signal is usually denoted as x(n), suppressing Ts in the argument of the function.

Consider the continuous complex exponential signal:

image.png

This mathematically well defined signal is predominantly used in signal and system analysis. To transition into the digital domain, we obtain the discrete signal by sampling the continuous signal, effectively replacing the variable t with nTs. Assuming a standard sampling interval, the discrete complex exponential signal elegantly becomes:

image.png

image.png

The graphical representation shown in your captured image perfectly visualizes the physical reality of replacing a continuous time variable with discrete mathematical steps.

Whether your discrete signal arises inherently or by sampling, the most important advantage is that it can be stored and processed efficiently using digital devices. This foundational transition from continuous to digital is precisely what enables the reliability of modern communication systems, ensuring that your RF transceivers and drone flight controllers operate with maximum computational efficiency.

Analog & Signal