  Particle beam microscopy uses a scanning beam of charged particles to create
images of samples, and the quality of image reconstruction suffers when this
beam current varies over time. Neither conventional reconstruction methods nor
time-resolved sensing acknowledges beam current variation, although through
sensitivity analysis, my project demonstrates that when the beam current
variation is appreciable, time-resolved sensing has significant improvement
compared to conventional methods in terms of image reconstruction quality,
specifically mean-squared error (MSE). To more actively combat this unknown
varying beam current's effects, my project further focuses on designing an
algorithm that uses time-resolved sensing for even better image reconstruction
quality in the presence of beam current variation. This algorithm works by
simultaneously estimating the unknown beam current variation in addition to the
underlying image, offering an alternative to more conventional methods, which
exploit statistical assumptions of the image content without explicitly
estimating the beam current. Using a concept of excess MSE due to beam current
variation, this algorithm provides a factor of 7 improvement on average, which
could lead to less expensive equipment in the future. Beyond improving the
image estimation, this algorithm offers a novel estimation of the beam current,
potentially providing more control in manufacturing and fabrication processes.
