The present invention provides a method for moving and transferring nanowires using tapered hair of diameter in micron range. The nanowires have a diameter of 60-150 nm. The tapered hair has a diameter of 1-100 m, a tip curvature radius of 0.8-3 m and a length of 4-10 mm. A plastic film on a copper grid used for a TEM is removed, the copper grid is reserved, and holes have a diameter of 50-100 m. The copper grid after ultrasonic cleaning gains the nanowires from the acetone liquid with ultrasonic dispersed nanowires. The copper grid with distributed nanowires and the tapered hair are respectively placed on mobile platforms of two different optical microscopes. Millimeter movement and micron movement of the tapered hair are realized, thereby realizing movement and transfer operation for the nanowires. The tip of the tapered hair is dipped in a small drop of conductive silver epoxy, and the conductive silver epoxy is respectively dropped on both ends of the nanowires; and the radius of the dropped conductive silver epoxy is 4-8 m. The present invention realizes a method for moving and transferring nanowires using tapered hair through the mobile platforms of the two optical microscopes.

Disclosed here is a scanning probe microscope system and method for operating the same for producing scanning probe microscope images at fast scan rates and reducing oscillation artifacts. In some embodiments, an inverse consistent image registration method is used to align forward and backward scan traces for each line of the scanning microscope image. In some embodiments, the aligned forward and backward scan traces are combined using a weighting factor favoring the scan trace with higher smoothness. In some embodiments, the scanning probe microscope image is a potentiometry map and a method is provided to extract from the potentiometry map a conductivity map.

Methods, devices, and systems for controlling a scanning tunneling microscope system are provided. In some embodiments, the methods, devices, and systems of the present disclosure utilize a control system included in or added to a scanning tunneling microscope (STM) to receive data characterizing a tunneling current between a tip of the scanning tunneling microscope system and a sample, to estimate, in real-time, a work function associated with the scanning tunneling microscope system, and to adjust, by a control system, a position of the tip based on an estimated work function. Associated systems are described herein.

Methods, devices, and systems for controlling a scanning tunneling microscope system are provided. In some embodiments, the methods, devices, and systems of the present disclosure utilize a control system included in or added to a scanning tunneling microscope (STM) to receive data characterizing a tunneling current between a tip of the scanning tunneling microscope system and a sample, to estimate, in real-time, a work function associated with the scanning tunneling microscope system, and to adjust, by a control system, a position of the tip based on an estimated work function. Associated systems are described herein.

Various examples are provided related to scanning tunneling thermometers and scanning tunneling microscopy (STM) techniques. In one example, a method includes simultaneously measuring conductance and thermopower of a nanostructure by toggling between: applying a time modulated voltage to a nanostructure disposed on an interconnect structure, the time modulated voltage applied at a probe tip positioned over the nanostructure, while measuring a resulting current at a contact of the interconnect structure; and applying a time modulated temperature signal to the nanostructure at the probe tip, while measuring current through a calibrated thermoresistor in series with the probe tip. In another example, a device includes an interconnect structure with connections to a first reservoir and a second reservoir; and a scanning tunneling probe in contact with a probe reservoir. Electrical measurements are simultaneously obtained for temperature and voltage applied to a nanostructure between the reservoirs.

Various examples are provided related to scanning tunneling thermometers and scanning tunneling microscopy (STM) techniques. In one example, a method includes simultaneously measuring conductance and thermopower of a nanostructure by toggling between: applying a time modulated voltage to a nanostructure disposed on an interconnect structure, the time modulated voltage applied at a probe tip positioned over the nanostructure, while measuring a resulting current at a contact of the interconnect structure; and applying a time modulated temperature signal to the nanostructure at the probe tip, while measuring current through a calibrated thermoresistor in series with the probe tip. In another example, a device includes an interconnect structure with connections to a first reservoir and a second reservoir; and a scanning tunneling probe in contact with a probe reservoir. Electrical measurements are simultaneously obtained for temperature and voltage applied to a nanostructure between the reservoirs.

Systems, methods and programs are provided for automated science experiments which use a model with learnt model parameters to define points for physical-characteristic measurements once the model is trained. The systems, methods and programs use active learning which enables describing a relationship between local features of sample-surface structure shown in image patches and determined representations of physical-characteristic measurements.

Systems, methods and programs are provided for automated science experiments which use a model with learnt model parameters to define points for physical-characteristic measurements once the model is trained. The systems, methods and programs use active learning which enables describing a relationship between local features of sample-surface structure shown in image patches and determined representations of physical-characteristic measurements.

Systems are provided for machine learning-driven operation of instrumentation with human in the loop. The systems use a model with learnt model parameters to define points for physical-characteristic measurements once the model is trained. The systems use active learning, which considers selection, reinforcement and/or adjustment inputs from the instrumentation's user, to enable describing a relationship between local features of sample-surface structure shown in image patches and determined representations of physical-characteristic measurements.

Systems are provided for machine learning-driven operation of instrumentation with human in the loop. The systems use a model with learnt model parameters to define points for physical-characteristic measurements once the model is trained. The systems use active learning, which considers selection, reinforcement and/or adjustment inputs from the instrumentation's user, to enable describing a relationship between local features of sample-surface structure shown in image patches and determined representations of physical-characteristic measurements.