The present application relates to a scanning probe microscope comprising a probe arrangement for analyzing at least one defect of a photolithographic mask or of a wafer, wherein the scanning probe microscope comprises: (a) at least one first probe embodied to analyze the at least one defect; (b) means for producing at least one mark, by use of which the position of the at least one defect is indicated on the mask or on the wafer; and (c) wherein the mark is embodied in such a way that it may be detected by a scanning particle beam microscope.

The present application relates to an apparatus for a scanning probe microscope, said apparatus having: (a) at least one first measuring probe having at least one first cantilever, the free end of which has a first measuring tip; (b) at least one first reflective area arranged in the region of the free end of the at least one first cantilever and embodied to reflect at least two light beams in different directions; and (c) at least two first interferometers embodied to use the at least two light beams reflected by the at least one first reflective area to determine the position of the first measuring tip.

This disclosure relates to automatic determination of locations of one or more closely spaced donor atoms implanted into a semiconductor crystal lattice. A processor receives image data generated by a scanning tunnelling microscope (STM). The image data is indicative of a tunnelling current between a scanning tip and the crystal lattice at multiple image locations. The processor applies a trained machine learning model to the image data to determine a classification into one of multiple candidate configurations of the one or more donor atoms. The multiple candidate configurations relate to different locations of the one or more donor atoms in the semiconductor crystal lattice. Based on an output of the trained machine learning model, the processor determines the location of the one or more donor atoms in the semiconductor crystal lattice.

The present invention relates to a device for operating at least one bending beam in at least one closed control loop, wherein the device has: (a) at least one first interface designed to receive at least one controlled variable of the at least one control loop; (b) at least one programmable logic circuit designed to process a control error of the at least one control loop using a bit depth greater than the bit depth of the controlled variable; and (c) at least one second interface designed to provide a manipulated variable of the at least one control loop.

The present invention relates to a device for operating at least one bending beam in at least one closed control loop, wherein the device has: (a) at least one first interface designed to receive at least one controlled variable of the at least one control loop; (b) at least one programmable logic circuit designed to process a control error of the at least one control loop using a bit depth greater than the bit depth of the controlled variable; and (c) at least one second interface designed to provide a manipulated variable of the at least one control loop.

An atomic force microscope is provided having a controller configured to store one or more positional parameters output by a sensor assembly when a light spot is located at a first preset position on the surface of the cantilever. The controller is further configured to operate an actuator assembly so as to induce movement of the spot away from the first preset position, to detect said movement of the first spot based on a change in the one or more positional parameters output by the sensor assembly, and to operate an optical assembly in response to the detected movement of the first spot to return the first spot to the first preset position.

An atomic force microscope is provided having a controller configured to store one or more positional parameters output by a sensor assembly when a light spot is located at a first preset position on the surface of the cantilever. The controller is further configured to operate an actuator assembly so as to induce movement of the spot away from the first preset position, to detect said movement of the first spot based on a change in the one or more positional parameters output by the sensor assembly, and to operate an optical assembly in response to the detected movement of the first spot to return the first spot to the first preset position.

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.

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.

A method creates a measurement plan of a dimensional measuring apparatus or controls a measurement of the dimensional measuring apparatus. The method includes receiving setting parameters defining a measurement or control command of multiple measurement or control commands of the dimensional measuring apparatus. The method includes evaluating the setting parameters based on at least one of a statistical evaluation and an evaluation using machine-assisted learning. The method includes determining a presetting that assigns at least one setting parameter of the evaluated setting parameters to the measurement or control command. The method includes outputting a setting parameter proposal based on the determined presetting in response to receiving an input command for selecting the measurement or control command.