Automated Transfer Program

Overview

This platform, developed by Austin Wu for the Yasuda Lab at Cornell, is designed for 2D material automation. It provides:

• Interface capabilities with various stations
• Flake searching functionality
• Potential automated stacking algorithms
• Automated station control

How to use

After installation, define wafers to scan over. Then trace over them and after compute where flakes are.

Prerequisites

• Miniconda (Make sure it's installed and added to your PATH)
• Python 3.11.9
• Node.js and npm (for the client application)

Installation

1. Set up the Python Environment

If on windows, ensure powershell is connected to conda porperly. You should see a (base) PATH before any command

# Create and activate a new conda environment
conda create -n automatedTransfer python=3.11.9
conda activate automatedTransfer

# Install dependencies
cd /path/to/project
pip install -r requirements.txt
conda install -n automatedTransfer -c conda-forge omero-py --no-update-deps # must use conda
pip install ".\thorlabs_tsi_camera_python_sdk_package.zip" # must use zip

Also ensure the proper dlls are inside of src/cameras/thor_drivers/ if you are using the thorcam. You can find more resournces at C:\Program Files\Thorlabs\Scientific Imaging\Scientific Camera Support

2. Install Software

The project requires the 2DMatGMM library as a submodule. Either use the submoudle recurisve or git clone it. Note please use my forked version.

# From the project root directory
git submodule update --init --recursive
git clone https://github.com/Austin4705/2DMatGMM

pip install -e 2DMatGMM

Run

I have created a file in the main directory launch.bat. By clicking it and running it all remaining things should either install or boot up.

Manually start up Program

If that doesnt work, you'll need to do this to run it:

This project also uses OMERO for storing microscope data. To install, simply run

dokcer compose pull

once in this directory to install it.

3. To Launch The Application

You will need to run three terminals to start up the app. Do the windows ones in powershell. Assume you open the terminal in this directory.

First Terminal

docker compose up -d

To run it on initalization ensure docker desktop is running in the background before running. If you want to view data, login at http://localhost:4080/ with usrname:password root:omero

Second Terminal

cd client
npm install

Third Terminal

conda activate automatedTransfer
cd src
python main.py

# Or use the shorthand command
conda activate automatedTransfer && cd src && python main.py

#Alternatively for windows you can run 
.\run.bat

4. Configure Specifci Environment

Create a .env file based on default.env in the project src directory with the following things:

• Transfer Station
  • Prior
  • Winfile
• Microscope:
  • Prior
  • usb

5. Notes

• Now if you are using a hq graphene transfer station, do a couple things. Ensure the software is updated to the latest version, it should have the latest version of the command server. Ensure that the imaging source driversare properly installed
• If on a hq graphene system ensure that the software is started and then goto file -> run command server
• Adjust the port values according to your setup.
• Well also need to install PYVisa. You can do this at the ni website it is free you just have to make an account
• You can make drivers to get other control platforms to work. Simply create a new inherited class and fill in the functions as needed. For debugging, there are vritual ones that do nothing
• If you encounter Conda-related issues on Windows (WSL is recommended instead):

Set-ExecutionPolicy Unrestricted
conda init powershell

Previously Useful Commands

{
  "type": "EXECUTE_TRANSFER_FUNCTION",
  "data": {
    "transfer_function_name": "SET_EXPOSURE_TIME",
    "parameters": "[{\"camera_index\": 0, \"exposure_time_us\": 5000}]"
  }
}

conda run -n automatedTransfer streamlit run flake_tuner.py --server.headless true 

localhost:8501

Contributing

For questions or contributions, please contact the Yasuda Lab at Cornell or Austin at austin-wu.com

License

Creative Commons BY-NC