Raspberry Pi Pico Oscilloscope: A Deep Dive

Hey guys! Ever thought about turning your Raspberry Pi Pico into a pocket-sized oscilloscope? It's totally doable, and we're going to walk through the ins and outs of how to make it happen. This project is not only super cool but also a fantastic way to learn about electronics, programming, and signal processing. Let's dive in!

What is a Raspberry Pi Pico Oscilloscope?

A Raspberry Pi Pico oscilloscope is essentially a digital oscilloscope built around the Raspberry Pi Pico, a small, low-cost microcontroller board. An oscilloscope is an instrument that visually displays electrical signals, showing voltage changes over time. Traditionally, oscilloscopes are standalone devices, but with the Pico, you can create a DIY version that's portable and surprisingly capable.

Why Build a Pico Oscilloscope?

Building your own oscilloscope has several advantages:

Educational Value: You'll gain hands-on experience with microcontroller programming, signal acquisition, and data processing.
Cost-Effective: A Pico is much cheaper than a dedicated oscilloscope.
Customization: You can tailor the software and hardware to your specific needs.
Portability: The Pico is small and easy to carry around, making your oscilloscope highly portable.

Key Components You'll Need

Before we get started, let's gather the necessary components:

Raspberry Pi Pico: The brains of the operation.
Micro USB Cable: For programming and power.
Breadboard and Jumper Wires: For prototyping the circuit.
Resistors: To protect the Pico's input.
Optional: LCD or OLED Display: For displaying the waveform directly (otherwise, you can use a computer).
Optional: Enclosure: To make your oscilloscope more robust and portable.

Setting Up Your Raspberry Pi Pico

First things first, you need to set up your Raspberry Pi Pico. This involves installing the necessary software and preparing the Pico for programming.

Installing MicroPython

MicroPython is a lean and efficient implementation of the Python 3 programming language, optimized for microcontrollers like the Raspberry Pi Pico. Here's how to get it installed:

Download the UF2 File: Go to the Raspberry Pi website and download the latest MicroPython UF2 file for the Pico.
Connect the Pico: Hold down the BOOTSEL button on your Pico while plugging it into your computer via USB. This puts the Pico into USB mass storage mode.
Copy the UF2 File: Your computer will recognize the Pico as a removable drive. Simply drag and drop the UF2 file onto the Pico.
The Pico Will Reboot: Once the file is copied, the Pico will automatically reboot and start running MicroPython.

Installing Thonny IDE

Thonny is a user-friendly Python IDE (Integrated Development Environment) that's perfect for working with MicroPython on the Raspberry Pi Pico. It simplifies the process of writing, uploading, and running code.

Download Thonny: Download the latest version of Thonny from the official website.
Install Thonny: Follow the installation instructions for your operating system.
Configure Thonny: Open Thonny and go to Tools > Options. In the Interpreter tab, select MicroPython (Raspberry Pi Pico) as the interpreter. This tells Thonny to communicate with your Pico.

Designing the Oscilloscope Circuit

Now comes the fun part: designing the circuit. The basic idea is to connect an input signal to one of the Pico's ADC (Analog-to-Digital Converter) pins. However, we need to protect the Pico from potentially damaging voltages.

Voltage Protection

The Raspberry Pi Pico's ADC pins can only handle voltages between 0V and 3.3V. If you try to measure a signal outside this range, you could damage the Pico. To prevent this, we use a voltage divider.

A voltage divider consists of two resistors in series. The input signal is connected to the top of the divider, and the Pico's ADC pin is connected to the midpoint. The resistors reduce the voltage to a safe level.

Choosing Resistor Values: Select resistor values that will scale the input voltage down to the 0-3.3V range. For example, using two equal resistors (e.g., 10kΩ each) will halve the input voltage.

Connecting the Circuit

Here's a simple circuit diagram:

Connect the input signal to one end of the first resistor.
Connect the other end of the first resistor to one end of the second resistor.
Connect the midpoint (the junction between the two resistors) to an ADC pin on the Pico (e.g., GP26).
Connect the other end of the second resistor to ground (GND).

Smoothing the Signal

To smooth out the signal and reduce noise, you can add a small capacitor in parallel with the second resistor. This creates a low-pass filter that attenuates high-frequency noise.

Choosing Capacitor Value: A capacitor value of around 0.1µF is usually a good starting point.

Writing the MicroPython Code

Now that your circuit is set up, it's time to write the MicroPython code that will read the ADC values and display them on your computer.

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Reading ADC Values

The following code reads the ADC value from a specified pin:

from machine import Pin, ADC
import time

adc_pin = ADC(Pin(26))  # GP26 is ADC0

while True:
    adc_value = adc_pin.read_u16()  # Read the ADC value (16-bit)
    voltage = adc_value * (3.3 / 65535)  # Convert to voltage
    print(f"ADC Value: {adc_value}, Voltage: {voltage:.2f} V")
    time.sleep(0.01)  # Sample every 10 ms

This code continuously reads the ADC value from pin GP26, converts it to a voltage, and prints it to the console. You can adjust the time.sleep() value to change the sampling rate.

Displaying the Waveform

To display the waveform, you'll need to send the ADC values to your computer and plot them using a graphing library like Matplotlib.

First, modify the MicroPython code to send the ADC values over the serial port:

from machine import Pin, ADC
import time
import sys

adc_pin = ADC(Pin(26))

while True:
    adc_value = adc_pin.read_u16()
    print(adc_value)  # Send the ADC value over serial
    sys.stdout.flush() # Force the output to be sent immediately
    time.sleep(0.001)  # Sample every 1 ms

Then, on your computer, use Python to read the serial data and plot it:

import serial
import matplotlib.pyplot as plt
import time

# Configure the serial port
ser = serial.Serial('COM3', 115200)  # Replace 'COM3' with your Pico's serial port

# Initialize the plot
plt.ion()
fig, ax = plt.subplots()
line, = ax.plot([], [])
ax.set_xlim(0, 100)  # Adjust as needed
ax.set_ylim(0, 65535)

xdata = []
ydata = []

start_time = time.time()

while True:
    try:
        # Read the ADC value from the serial port
        adc_value = int(ser.readline().decode('utf-8').strip())

        # Update the plot data
        xdata.append(time.time() - start_time)
ydata.append(adc_value)

        # Limit the number of data points
        if len(xdata) > 100:
            xdata.pop(0)
ydata.pop(0)

        # Update the plot
        line.set_xdata(xdata)
line.set_ydata(ydata)
ax.relim()
ax.autoscale_view()
fig.canvas.draw()
fig.canvas.flush_events()

    except ValueError:
        print("Invalid data received")
    except KeyboardInterrupt:
        break

# Close the serial port
ser.close()

Important Considerations:

Adjust the serial port (COM3 in the example) to match your Pico's serial port.
The baud rate (115200) should match the default baud rate of the Pico's serial port.
The set_xlim() and set_ylim() values may need to be adjusted depending on the signal you're measuring.
You will need to install pyserial and matplotlib: pip install pyserial matplotlib

Adding a Display

For a truly portable oscilloscope, you can add an LCD or OLED display to the Pico. This eliminates the need to connect to a computer to see the waveform.

Connecting the Display: Connect the display to the Pico using the appropriate pins (usually SPI or I2C).
Writing Display Code: Use a MicroPython library like ssd1306 (for OLED displays) or machine.SPI and machine.Pin (for LCDs) to draw the waveform on the display. This will require more advanced programming skills, but it's a rewarding challenge.

Improving Performance

The basic oscilloscope we've built so far is functional, but its performance is limited by the Pico's ADC speed and the overhead of MicroPython. Here are some ways to improve performance:

Overclocking the Pico

You can overclock the Raspberry Pi Pico to increase its clock speed and improve ADC sampling rates. Be cautious when overclocking, as it can cause the Pico to overheat or become unstable.

How to Overclock: Use the machine.freq() function to set the clock frequency. For example, machine.freq(250000000) sets the clock frequency to 250 MHz.

Using DMA

DMA (Direct Memory Access) allows the ADC to transfer data directly to memory without involving the CPU. This can significantly improve sampling rates.

Implementing DMA: Use the rp2.DMA class to configure a DMA channel to transfer ADC data to a buffer in memory.

Optimizing the Plotting Code

The plotting code on your computer can also be a bottleneck. Use efficient plotting techniques and avoid unnecessary calculations.

Use Fast Plotting Functions: Matplotlib has several functions for fast plotting, such as plot() with the '-' style option.
Reduce Data Points: If you're plotting a large number of data points, consider reducing the number of points to improve performance.

Conclusion

So, there you have it! Building a Raspberry Pi Pico oscilloscope is a fun and educational project that allows you to create a portable and customizable test instrument. While it may not replace a professional oscilloscope, it's a great tool for learning about electronics and signal processing. Get your hands dirty, experiment with different circuits and code, and see what you can create!

Have fun building, and let me know if you have any questions or cool modifications you come up with!