NON-DESTRUCTIVE FLAW DETECTION AND PROFILING IN FERROMAGNETIC AND PARAMAGNETIC MATERIALS USING QUANTUM WELL HALL EFFECT (QWHE) SENSORS

Abstract

Quantum Well Hall Effect (QWHE) sensors are compound semiconductor magnetic sensors with properties that make them well suited for magnetic imaging (small footprint, nanotesla sensitivity, high amplitude and frequency linearity, low power consumption). The work presented in this thesis focuses on the development of QWHE imaging systems for the detection and characterisation of paramagnetic and ferromagnetic materials. The first half of this thesis discusses the development of a single sensor QWHE imaging system for Eddy Current Testing (ECT) of surface breaking flaws in paramagnetic materials. The development focussed particularly on the design and characterisation of coils for electromagnetic illumination, where a single QWHE sensor would measure the magnetic field response from the sample. It was found at this stage that the use of smaller coils allowed for overall stronger fields to be generated, along with a narrower field distribution offering higher resolution images, and finally a higher Self Resonant Frequency (SRF), allowing for higher magnetic imaging frequencies which in turn offered higher Signal to Noise Ratios (SNR). Of the coil geometries tested, the most optimal variant found had a diameter of approximately 2 mm, with 20 turns, and could produce up to 1.2 mT and frequencies up to 400 kHz. Using this coil, the equivalent flaw diameter was approximately 4 mm. Comparing this with the other coils developed here, The SNRs ranged from 200 to 400 kHz, with maximum field strengths of 200 - 1200 µT. After the development of the single sensor system, work moved on to image processing and analysis of images taken using Magnetic Flux Leakage (MFL), and images collected by the previously described ECT system. The impact of both coil diameter and imaging resolution were explored. It was found that larger coils effectively act as an image domain low pass filter placed before the measurements are taken, increasing the width of the flaw responses. When combining this with Nyquist sample theorem, it was found that larger coils would allow for courser image sampling. This suggested that flaw imaging could be performed by arrays with sensor spacing greater than the minimum flaw size present, provided the illumination coil was sufficiently large to significantly increase the flaw width response to make it readable by a coarse sensor array. The most significant development made during the course of this PhD was the implementation of Time Frequency Division Multiplexing (TFDM) for QWHE arrays. This method works by biasing different QWHE sensors at different frequencies, thereby shifting their respective measurement frequencies. The outputs of multiple shared frequency channels are then multiplexed in the time domain. This method was used to develop a 16x16 sensor array with an integrated illuminating coil. The system ran optimally at a frame rate of 8 fps, and could detect ferromagnetic objects at lift offs up to 30 mm, and paramagnetic objects up to 15 mm. The system was also capable of imaging 5 mm width surface breaking machined defects in a steel sample, with a noise floor equivalent to around 10 µT. The last section of this thesis looks at a method of simplifying the required routing for a QWHE TFDM system. This method relies on the sensor outputs being passively summed through resistors before a single multiplexer and amplifier. This method greatly reduces both the length and the number of connections needed. This offers for reduced magnetic pickup and a lower component count, not including the summing resistors. A 4x4 sensor test system was built to determine the feasibility of this method, and it was found that AC magnetic fields can indeed be measured, although some gain and offset correction needs to be performed in software which is performed as a matter of routine in modern imaging systems.

Date of Award	31 Dec 2023
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Mohamed Missous (Supervisor) & Krikor Ozanyan (Supervisor)