The silicon based photonic spectrometer was installed into a 3U CubeSat (10cm x 10cm x 30cm) in order to detect up to 6 wavelength bands. As light is transmitted through a gaseous medium, the filtering of wavelengths at absorption bands of specific elements is done by method of ring resonation. While detection takes place with a fiber coupled camera interpreted by our RaspberryPi. Future iterations of the project will host further optimized componentry for better precision with a goal of detecting life supporting elements in exoplanetary atmospheres for further science and exploration. 

The CubeSat platform was chosen as the cost of instrumentation and launches are significantly lower than specific mission satellites as the CubeSat platform is a standard that allows scientists to mount instrumentation for a fraction of the cost by streamlining the integration of satellites for launch. 

Starting the project in September of 2023, our team moved quickly to advance the integration of instrumentation in the CubeSat platform to complete a working model that detects light transmission through the photonic chip in the visual and infrared bands in order to detect a signal, proving the viability of silicone based photonic chips for spectroscopy for use in space exploration and astronomy based science. 

The project aims to develop a CubeSat equipped with a photonic chip for the purpose of conducting atmospheric analysis of exoplanets. The choice of spectrometer was due to its unique ability to detect a small wavelength range in which it can provide intensity data across absorption bands.

The project focused on the development of photonic chip integration into the CubeSat platform for atmospheric analysis. The platform provides a cost-effective platform for space-based observations. 

The primary instrument involves a silicon-based photonic chip which analyzes major absorption bands of elements such as carbon dioxide and methane at 1580 nm & 1640 nm respectively. Additional instrumentation is required to support its ability to gather and transmit data including solar cells, battery(s), on board computation, cameras and an optical system. The chip's design is based on proven methods with adaptations for multi-wavelength analysis.

Data is analyzed using light detection techniques to identify the presence of a signal. Analysis of the signal is preformed using Python, with libraries such as NumPy, Picamera2, OpenCV, and Matplotlib for processing and visualization. 

Fiber componentry placement and orientation proved challenging as fiber losses through radii limitations were pushed while maintaining signal integrity. Fiber coiling techniques were strategically used to remedy excess fiber length. 

Design would take four months while assembly took the following three, while software development took place over the entire course of the project. The final 2 months of the project are used to solve ongoing issues or implement further capabilities within the machine.

Future developments of the project will take place at UofT.