  This paper presents a method for hardware trojan detection in integrated
circuits. Unsupervised deep learning is used to classify wide field-of-view
(4x4 mm$^2$), high spatial resolution magnetic field images taken using a
Quantum Diamond Microscope (QDM). QDM magnetic imaging is enhanced using
quantum control techniques and improved diamond material to increase magnetic
field sensitivity by a factor of 4 and measurement speed by a factor of 16 over
previous demonstrations. These upgrades facilitate the first demonstration of
QDM magnetic field measurement for hardware trojan detection. Unsupervised
convolutional neural networks and clustering are used to infer trojan presence
from unlabeled data sets of 600x600 pixel magnetic field images without human
bias. This analysis is shown to be more accurate than principal component
analysis for distinguishing between field programmable gate arrays configured
with trojan free and trojan inserted logic. This framework is tested on a set
of scalable trojans that we developed and measured with the QDM. Scalable and
TrustHub trojans are detectable down to a minimum trojan trigger size of 0.5%
of the total logic. The trojan detection framework can be used for golden-chip
free detection, since knowledge of the chips' identities is only used to
evaluate detection accuracy
