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.

A method of examining a sample (1) in a microscope equipped with at least one scanning tunneling tip (4), wherein tunneling current TC can be detected and wherein relative tip-to-sample planar coordinates xrel=xtipxsample and yrel=ytipysample are differences between corresponding tip and sample coordinates, wherein the following steps are performed above at least two surface points of the sample (1): placing the tip (4) successively above said surface points of the sample (1); above each of said surface points of the sample, performing a distance varying step (33) comprising varying the tip-to-sample distance D, and performing a time dependencies recording step (35), comprising recording time dependencies TC(t), xrel(t), yrel(t), D(t). Based on these time dependencies, four maps can be created from a single measurement: constant current map, constant height map, Local Density of States Topography map and potential barrier map.

A method of examining a sample (1) in a microscope equipped with at least one scanning tunneling tip (4), wherein tunneling current TC can be detected and wherein relative tip-to-sample planar coordinates xrel=xtipxsample and yrel=ytipysample are differences between corresponding tip and sample coordinates, wherein the following steps are performed above at least two surface points of the sample (1): placing the tip (4) successively above said surface points of the sample (1); above each of said surface points of the sample, performing a distance varying step (33) comprising varying the tip-to-sample distance D, and performing a time dependencies recording step (35), comprising recording time dependencies TC(t), xrel(t), yrel(t), D(t). Based on these time dependencies, four maps can be created from a single measurement: constant current map, constant height map, Local Density of States Topography map and potential barrier map.

There is provided an iridium tip including a pyramid structure having one {100} crystal plane as one of a plurality of pyramid surfaces in a sharpened apex portion of a single crystal with <210> orientation. The iridium tip is applied to a gas field ion source or an electron source. The gas field ion source and/or the electron source is applied to a focused ion beam apparatus, an electron microscope, an electron beam applied analysis apparatus, an ion-electron multi-beam apparatus, a scanning probe microscope or a mask repair apparatus.

There is provided an iridium tip including a pyramid structure having one {100} crystal plane as one of a plurality of pyramid surfaces in a sharpened apex portion of a single crystal with <210> orientation. The iridium tip is applied to a gas field ion source or an electron source. The gas field ion source and/or the electron source is applied to a focused ion beam apparatus, an electron microscope, an electron beam applied analysis apparatus, an ion-electron multi-beam apparatus, a scanning probe microscope or a mask repair apparatus.

A computer implemented method for scanning tunneling microscopy is described. The method includes scanning a sample surface at a specific sample bias voltage using a scanning tunneling microscope in constant-current mode; and decoupling effects of surface topology variations from effects of surface conductivity variations comprising estimating surface conductivity, , using Kalman filtering comprising modeling process noise as colored noise.

A computer implemented method for scanning tunneling microscopy is described. The method includes scanning a sample surface at a specific sample bias voltage using a scanning tunneling microscope in constant-current mode; and decoupling effects of surface topology variations from effects of surface conductivity variations comprising estimating surface conductivity, , using Kalman filtering comprising modeling process noise as colored noise.

An adapter kit for transferring a sample between a first vacuum instrument and a second vacuum instrument. The kit includes a substantially planar sample holder comprising at least one holder tab, a transfer plate comprising a sample region, at least one plate tab, and at least one connection site, all disposed outside of the sample region, and a transfer adaptor comprising at least one protrusion adapted to be connected to the transfer plate via the at least one connection site to enable rotation and translation of the transfer plate. The plate tab is insertable behind a backside of the holder tab by a rotational movement of the transfer plate.

A method for autonomously applying a dangling bond pattern to a substrate for atom scale device fabrication includes inputting the pattern, initiating a patterning process, scanning the substrate using a scanning probe microscope (SPM) to generate an SPM image of the substrate, feeding the SPM image into a trained convolution neural network (CNN), analyzing the SPM image using the CNN to identify substrate defects, determining a defect free substrate area for pattern application; and applying the pattern to the substrate in that area. An atom scale electronic component includes functional patches on a substrate and wires electrically connecting the functional patches. Training a CNN includes recording a Scanning Tunneling Microscope (STM) image of the substrate, extracting images of defects from the STM image, labeling pixel-wise the defect images, and feeding the extracted and labeled images of defects into a CNN to train the CNN for semantic segmentation.