Systems and methods for automated tip conditioning for scanning tunneling spectroscopy
A scanning tunneling microscope including a z-axis scanning assembly; a quantum tunneling tip operatively connected to the z-axis scanning assembly; a z-axis controller configured to communicate with the z-axis scanning assembly; an x-y scanning assembly including a sample platform for holding a sample to be observed and arranged proximate the quantum tunneling tip separated in a z-axis direction from the platform; an x-y controller configured to communicate with the x-y scanning assembly; a measurement circuit connected to the quantum tunneling tip and the sample platform such that a relative electrical voltage is provided between said quantum tunneling tip and the sample platform and so as to measure an electrical current; and a data processor configured to communicate with the z-axis controller and the x-y controller to receive surface imaging information or point spectral information therefrom.
1. A scanning tunneling microscope, comprising:
a z-axis scanning assembly;
a quantum tunneling tip operatively connected to said z-axis scanning assembly;
a z-axis controller configured to communicate with said z-axis scanning assembly;
an x-y scanning assembly comprising a sample platform having a surface for holding a sample to be observed and arranged proximate said quantum tunneling tip separated in a z-axis direction from said surface;
an x-y controller configured to communicate with said x-y scanning assembly;
a measurement circuit connected to said quantum tunneling tip and said sample platform such that a relative electrical voltage between said quantum tunneling tip and said sample platform can be provided during operation and such that an electrical current due to quantum mechanical tunneling from a sample held on said sample platform and said quantum tunneling tip can be measure, said measurement circuit being in communication with said z-axis controller to provide measurements of said electrical current; and
a data processor configured to communicate with said z-axis controller and said x-y controller to receive one of surface imaging information or point spectral information therefrom,
wherein said data processor is further configured to provide instructions to said z-axis controller and said x-y controller for automatic conditioning of said quantum tunneling tip.
2. The scanning tunneling microscope according to claim 1 , wherein said data processor is further configured to automatically locate a conditioning region within a scanning tunneling surface map for quantum tunneling tip conditioning.
3. The scanning tunneling microscope according to claim 2 , wherein said data processor is further configured to automatically provide instructions to said x-y controller to position said quantum tunneling tip over said conditioning region, to automatically provide instructions to said z-axis controller to move said quantum tunneling tip to come into contact and then retract from contact with said conditioning region, to automatically provide instructions to said x-y controller to re-position said quantum tunneling tip over said conditioning region, to provide instructions to said z-axis controller to perform scanning tunneling spectroscopy measurements, and to compare said scanning tunneling spectroscopy measurements to a model to determine whether said quantum tunneling tip is in condition for use.
4. The scanning tunneling microscope according to claim 3 , wherein said data processor is further configured to automatically repeat providing instructions to said x-y controller to position said quantum tunneling tip over said conditioning region, to automatically repeat providing instructions to said z-axis controller to move said quantum tunneling tip to come into contact and then retract from contact with said conditioning region, to automatically repeat providing instructions to said x-y controller to re-position said quantum tunneling tip over said conditioning region, to automatically repeat providing instructions to said z-axis controller to perform scanning tunneling spectroscopy measurements, and to automatically repeat comparing said scanning tunneling spectroscopy measurements to a model until it automatically determines that said quantum tunneling tip is in condition for use.
5. The scanning tunneling microscope according to claim 3 or 4 , wherein said model is a machine learning model resulting from training on a plurality of data sets.
6. A method for automated tip conditioning for a scanning tunneling microscope, comprising:
receiving by a computer one of surface imaging information or point spectral information from a quantum tunneling tip;
locating by the computer a conditioning region within a scanning tunneling surface map for quantum tunneling tip conditioning;
providing instructions by the computer to position said quantum tunneling tip over said conditioning region;
providing instructions by the computer to move said quantum tunneling tip to come into contact and then retract from contact with said conditioning region;
providing instructions by the computer to re-position said quantum tunneling tip over said conditioning region;
providing instructions by the computer to perform scanning tunneling spectroscopy measurements; and
providing instructions by the computer to compare said scanning tunneling spectroscopy measurements to a model to determine whether said quantum tunneling tip is in condition for use.
7. The method of claim 6 , further comprising repeating a plurality of times until a stopping criterion is met at least the following:
providing instructions by the computer to position said quantum tunneling tip over said conditioning region;
providing instructions by the computer to move said quantum tunneling tip to come into contact and then retract from contact with said conditioning region;
providing instructions by the computer to re-position said quantum tunneling tip over said conditioning region;
providing instructions by the computer to perform scanning tunneling spectroscopy measurements; and
providing instructions by the computer to compare said scanning tunneling spectroscopy measurements to a model to determine whether said quantum tunneling tip is in condition for use.
8. The method of claim 6 or claim 7 , wherein said model is a machine learning model resulting from training on a plurality of data sets.
9. A non-transitory computer-readable medium storing a computer-executable code which when executed by a computer system causes the computer system to perform the method of claim 8 .
10. A non-transitory computer-readable medium storing a computer-executable code which when executed by a computer system causes the computer system to perform the method of claim 6 or claim 7 .
11. A computer-implemented method for automated tip conditioning for a scanning tunneling microscope, wherein the method is implemented in a computer system comprising one or more processors configured to execute one or more computer program modules, the method comprising:
receiving by the computer system one of surface imaging information or point spectral information from a quantum tunneling tip;
locating by the computer system a conditioning region within a scanning tunneling surface map for quantum tunneling tip conditioning;
providing instructions by the computer system to position said quantum tunneling tip over said conditioning region;
providing instructions by the computer system to move said quantum tunneling tip to come into contact and then retract from contact with said conditioning region;
providing instructions by the computer to re-position said quantum tunneling tip over said conditioning region;
providing instructions by the computer system to perform scanning tunneling spectroscopy measurements; and
providing instructions by the computer system to compare said scanning tunneling spectroscopy measurements to a model to determine whether said quantum tunneling tip is in condition for use.