Image charge detector (ICD) technology has existed for decades. However, not until recently has an ICD been proposed for use in space exploration, specifically for studying the characteristics of the dust on Mars. Characterizing the dust on Mars is crucial for designing equipment to aid manned missions. It also improves our understanding of Mars' climate and weather systems. An ICD utilizing printed circuit board (PCB) electrodes, coupled with a custom differential amplifier, is best suited for this type of measurement because of its light weight, simplicity, and noise performance. The noise floor of our particular amplifier is measured to be 1030 e- and simulated to be as low as 140 e-. Both of these measurements are taken without averaging. To further verify and understand this device, I developed a novel simulation method using ANSYS Maxwell 3D to simulate the interaction between the charged particle and the electrodes of the ICD. The results from this simulation are then easily passed to Cadence where we can clearly see the response of the custom amplifier to the charged particle. This knowledge is used to study various types of electrode geometry for improved noise performance, as well as understand how particle trajectory affect the resulting signal. Once the validity of the Maxwell simulation is established, I use it, along with experimental data and a mathematical model based on conformal mapping, to optimize the ICD for noise performance. I find that the maximum noise performance does not lie in simply increasing the number of sensing stages, as was previously thought. The optimum number of stages is a function of the parasitic capacitance of the amplifier, with the greater parasitic capacitance leading to the greater number of stages for the optimum.
College and Department
Ira A. Fulton College of Engineering and Technology
BYU ScholarsArchive Citation
Rozsa, Jace, "Characterization and Optimization of an Image Charge Detector for the Measurement of Martian Dust" (2020). Theses and Dissertations. 8700.
mass spectrometry, finite element model, optimization, modeling