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PhD Final Dissertation Defense: Andreu Glasman
<P>Title:Numerical simulation and data-driven analysis of infrared detector performance </P><P>Advisor:Prof. Enrico Bellotti, (ECE, MSE)</P><P>Chair:TBD</P><P>Committee:Prof. Roberto Paiella, (ECE, MSE); Prof. Sahar Sharifzadeh, (ECE, MSE); Dr. Marion Reine, Consultant on Infrared Detectors</P><P>Abstract:Infrared detectors are a critical technology frequently used in commercial, military, and scientific settings. Research and development of modern infrared detectors is driven by finding new ways to reduce the system's size, weight, power, and cost while adding new functionalities without lowering performance. Physics-based numerical models can be instrumental in lowering the cost of developing such advances. This dissertation presents three main contributions to further the predictive modeling of infrared photodetectors.</P><P>First, motivated by recent demonstrations of small-pitch focal plane arrays for infrared imaging---5 μm for SWIR and 10 to 15 μm for MWIR/LWIR---we use physics-based numerical simulations to assess the implications on dark current, quantum efficiency, specific detectivity, and modulation transfer function in SWIR InGaAs FPAs. From the results, we propose a new pixel sub-architecture aimed toward lowering dark current and improving MTF.</P><P>Second, we present a methodology for simulating the capacitance-voltage (C-V) characteristics of nBn photodetectors. For junction-based semiconductor devices, C-V profiling is a common technique for non-destructively characterizing semiconductor layers in metal-insulator-semiconductor devices by using well-established analytical relations. However, this type of analysis cannot be directly applied to the barrier detector’s unique architecture and the formalism must be modified. To this end, we present a modified analytical formalism based on metal-oxide-semiconductor theory, and a methodology using the drift-diffusion method; both are used to explore the role of the device architectural properties on determining the C-V characteristics.</P><P>Last, we present several cases of applying neural networks to nBn photodetector figures of merit. We use artificial neural networks as surrogate model for the capacitance-voltage, current-voltage, and quantum efficiency to explore the multi-dimensional parameter space to assess parameter-performance correlations and determine the global role of each feature in shaping each characteristic without the need for additional simulations. Moreover, using inspiration from image recognition, we demonstrate that a convolutional neural network can be trained to analyze a C-V characteristic to yield more information about a device than what would be possible from a conventional analysis.</P>
| When | 1:00 pm to 3:00 pm on Thursday, August 27, 2020 |
|---|---|
| Location | Zoom ID: 978 8117 7503 |