Discrimination of nano-objects via cluster analysis techniques applied to time-resolved thermo-acoustic microscopy

Time-effective, unsupervised clustering techniques are exploited to discriminate nanometric metal disks patterned on a dielectric substrate. The discrimination relies on cluster analysis applied to time-resolved optical traces obtained from thermo-acoustic microscopy based on asynchronous optical sampling. The analysis aims to recognize similarities among nanopatterned disks and to cluster them accordingly. Each cluster is characterized by a fingerprint time-resolved trace, synthesizing the common features of the thermo-acoustics response of the composing elements. The protocol is robust and widely applicable, not relying on any specific knowledge of the physical mechanisms involved. The present route constitutes an alternative diagnostic tool for on-chip nondestructive testing of individual nano-objects.

Automated summary

Time-effective, unsupervised clustering techniques are used to discriminate nanometric metal disks on a dielectric substrate based on time-resolved optical traces. The clusters aim to recognize similarities among nanopatterned disks and characterize them accordingly, providing an alternative diagnostic tool for nondestructive testing of individual nano-objects on chips.