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 cluster analysis applied time-resolved optical traces obtained from thermo-acoustic microscopy based asynchronous sampling. aims recognize similarities among nanopatterned and them accordingly. Each is characterized by fingerprint trace, synthesizing the common features of thermo-acoustics response composing elements. protocol robust widely applicable, not relying any specific knowledge physical mechanisms involved. present route constitutes an alternative diagnostic tool for on-chip non-destructive testing individual nano-objects.