System and method for autonomous scanning probe microscopy with in-situ tip conditioning

Abstract

A method for assessing the quality of a tip of a scanning probe microscope (SPM) includes recording an SPM image, extracting a plurality of images of dangling bonds from the SPM image, feeding the extracted images of dangling bonds into a convolution neural network one image at a time, analyzing each of the plurality of images of dangling bonds using the convolution neural network, assigning each of the plurality of images of dangling bonds one of a sharp tip status or a double tip status, and determining whether the number of the plurality of images of dangling bonds of the SPM image assigned the double tip status exceeds a predetermined threshold. A method of automatically conditioning a tip of a scanning probe microscope (SPM) during imaging of a sample and a method of mass-producing atomistic quantum dots, qubits, or particular atom orbital occupation are also provided.

1 . A system comprising:
 a scanning probe microscope (SPM) with a probe tip, where an automated assessment of the quality of the probe tip is determined while the probe tip is scanning or atomically manipulating a surface of a given sample, said system further comprising:   machine learning algorithms that monitor and assess probe tip condition and determine when probe tip reconditioning is required;   a recording device configured to store a series of SPM images; and   wherein the system is configured to:   extract a plurality of images of dangling bonds from each of the SPM images;   feed the plurality of extracted images of dangling bonds into a convolution neural network one image at a time;   analyze each of the plurality of extracted images of dangling bonds using the convolution neural network;   assign each of the plurality of extracted images of dangling bonds one of a sharp tip status or a double tip status; and   determine whether the number of the plurality of extracted images of dangling bonds of the SPM image assigned the double tip status exceeds a predetermined threshold.   
     
     
         2 . The system of  claim 1  wherein the convolution neural network trains the machine learning algorithms. 
     
     
         3 . The system of  claim 1  wherein the SPM image is recorded at a sample bias of −1.8 V. 
     
     
         4 . The system of  claim 1  wherein the SPM image is recorded at 50 pA. 
     
     
         5 . The system of  claim 1  wherein the plurality of extracted images of dangling bonds appear as bright protrusions in the SPM image recorded. 
     
     
         6 . The system of  claim 1  wherein each of the plurality of extracted images of dangling bonds are 6×6 nm 2 . 
     
     
         7 . The system of  claim 1  wherein the SPM image recorded is 100×100 nm 2 . 
     
     
         8 . The system of  claim 1  wherein the sample is a hydrogen-terminated Si(100) surface. 
     
     
         9 . The system of  claim 1  wherein the convolution neural network includes a first convolution layer, a second convolution layer, a pooling layer, a densely connected layer, and an output layer. 
     
     
         10 . The system of  claim 1  wherein the analysis of each of the plurality of extracted images of dangling bonds includes pixilation of each of the images of dangling bonds. 
     
     
         11 . The system of  claim 1  further comprising an interface to alert a user of the SPM when the number of the plurality of extracted images of dangling bonds of the SPM image assigned the double tip status exceeds the predetermined threshold. 
     
     
         12 . The system of  claim 1  wherein the machine learning algorithms are further configured to set an image frame and a location within the image frame to automatically condition the tip when it is determined that a number of the plurality of extracted images of dangling bonds of the SPM image within the image frame assigned a double tip status exceeds the predetermined threshold. 
     
     
         13 . The system of  claim 12  wherein an assessment of the quality of the tip of the scanning probe microscope (SPM) is performed within the image frame on the sample until it is determined that the number of the plurality of extracted images of dangling bonds of the SPM image within the image frame assigned the double tip status exceeds the predetermined threshold. 
     
     
         14 . The system of  claim 12  wherein the location of tip conditioning is a location on the sample outside of the image frame. 
     
     
         15 . The system of  claim 12  wherein the location for tip conditioning is a location where the tip is conditioned when it is determined that the number of the plurality of images of dangling bonds of the SPM image within the image frame assigned the double tip status exceeds the predetermined threshold. 
     
     
         16 . A system that mass produces atomistic quantum dots, qubits, or particular atom orbital occupation, the system comprising:
 a scanning probe microscope (SPM) having a probe tip that scans or atomically manipulates the surface of a given sample, said system further comprising:   machine learning algorithms that monitor and assess probe tip condition and determine when tip reconditioning is required;   a recording device configured to store a series of SPM images; and   wherein the system is configured to:   extract a plurality of images of dangling bonds from each of the SPM images;   feed the extracted images of dangling bonds into a convolution neural network one image at a time;   analyze each of the plurality of extracted images of dangling bonds using the convolution neural network;   assign each of the plurality of extracted images of dangling bonds one of a sharp tip status or a double tip status; and   determine whether the number of the plurality of extracted images of dangling bonds of the SPM image assigned the double tip status exceeds a predetermined threshold; and   wherein the system is further configured to:   selectively sense or modify an orbital occupation state of a given atom with the SPM;   repeat the selective sensing or modifying of the orbital occupation state of a plurality of additional individual atoms with the SPM;   repair the SPM or replace the SPM with a new SPM; and   repeat the selective sensing or modifying the orbital occupation state of a second plurality of additional individual atoms with the repaired SPM or the new SPM.   
     
     
         17 . The system of  claim 16  wherein the convolution neural network trains the machine learning algorithms. 
     
     
         18 . The system of  claim 16  wherein the convolution neural network includes a first convolution layer, a second convolution layer, a pooling layer, a densely connected layer, and an output layer. 
     
     
         19 . The system of  claim 16  wherein the plurality of images of dangling bonds appear as bright protrusions in the SPM image recorded. 
     
     
         20 . The system of  claim 16  wherein the sample is a hydrogen-terminated Si(100) surface.