The present disclosure is generally related to systems, tools, software, control, and methods for a semi-auto scanning probe microscope techniques.
In some embodiments, a method can include manually teaching a first sample and atomic force microscopy (“AFM”) tip relative location on an AFM tool. The method may then scan, via an automated program, the sample with the AFM tool to produce one or multiple images of the sample.
In further embodiments, a device may include an atomic force microscopy (“AFM”) tool having an AFM tip. The device may be adapted to allow a user to manually position a first sample relative to the AFM tip and then automatically scan the other samples that has the same pattern with the AFM tip to produce images for all of the rest samples based on an initial teaching on the first sample.
In the following detailed description of the embodiments, reference is made to the accompanying drawings which form a part hereof, and in which are shown by way of illustration of specific embodiments. It is to be understood that other embodiments may be utilized and changes may be made without departing from the scope of the present disclosure.
Scanning probe microscope techniques may be used for imaging and characterizing surface topology and properties at atomic resolution, such as for nanotechnology and nanoscience. Specifically, atomic force microscopy (“AFM”) (which is one type of scanning probe microscope techniques) can be used as a metrology tool in nanotechnology manufacturing, and specifically in nanoelectronic device manufacturing. AFM has applications to determine topography, shape, dimensions, and other potential uses, such as in semiconductors, photolithography and photomasks, and devices implementing thin film technologies, such as a transducer for magnetic data storage.
Manufacturing devices on a microscale or nanoscale can involve a series of complex fabrication process steps, such as by sequential layering on a substrate. To achieve a goal of high quality and low cost, the manufacturing process may include various metrology and inspection steps within a manufacturing line, such as to monitor density, pattern geometry, shape, dimensions, topography, thickness, or material composition. Further, calibrations to devices or systems, or to the manufacturing process itself, may be made based on the measurements or information received from the various metrology and inspection steps. The various metrology and inspection steps may often be on a micron or nanometer scale; for example, a transistor gate width may approximately be in a range of 32 nm to 35 nm. AFM may be used in semiconductor fabrication as a dimension metrology tool, such as for etching and chemical mechanical polishing characterization. Similar process technologies may be used in the photomask industries and thin film industries, as well as applications in biology and medical devices. AFM allows a quick survey of a cross-sectional profile or surface topography to examine if a dimension is in specification, without destroying a product.
An atomic force microscope can scan a region of a sample that is highly localized and can be anywhere, as long as the space permits the tip size. The measurement may be generally free of bias arising from the target shape, pattern proximity, density, and material. With a feedback control loop, the atomic force microscope scanner can control a tiny probe to perform scanning motion in x (or y) and z directions to maintain a close proximity between the probe and sample surface, acquiring high-resolution positional data in all x, y, and z axes. A two or three dimensional topographic image can be constructed from the x/y/z spatial data. The scanned pattern or topographic image may be any shape, such as a rectangle, square, octagon, or any other shape.
Once a topographic image is constructed, offline (i.e. not necessarily done within the AFM tool) software analysis can de-convolute the tip shape from the AFM images and extract important geometric parameters about the measured target, such as depth, line width at top/middle/bottom locations, sidewall angle and profile shape, or surface topography.
AFM may be implemented on a completely manual AFM tool; however, performing such measurements could be very tedious and difficult. This can affect a throughput in a production environment. AFM may also be implemented as a completely automated process for industrial manufacturing applications, such as by programming a recipe (such as a pre-determined program which may include defining AFM tip and sample relative locations, setting proper scanning parameters, and execute series scans at the same or different locations) into an AFM tool for automated metrology measurements. A completely automated based AFM system can perform the process, but a lot of hardware is required for tip alignment and pattern recognition. Thus, the costs of a fully automated AFM tool can be relatively high, such as three or four times higher than a manual tool. The methods and systems disclosed herein are designed to solve such problems by enabling a semi-automatic or even fully automatic (or semi-automated or automated) measurement(s) on a manual AFM tool.
In a first example, referring to
The method 100 can include locating a sample or part on an AFM tool, at 102, that may be capable of processing multiple samples or devices. For example, this may include locating a sample within a certain proximity to the AFM tip. This can be done manually. For example, an operator may manually load the part, such as by using a step motor. The placement of the part may be visible to an operator on a display.
Then, the method 100 may teach the relative position of the AFM tip and first sample or device, at 104. The method 100 may also create a sample and tip location marks on a control computer screen, also at 104. The software of the AFM tool then can define the sample and tip location for further samples. Further, the AFM tool may include a display to show an image to the operator to allow the operator to manually select a location, via a cursor, as the starting location of a tip.
To enhance location accuracy, a unique feature for locating scanning locations may be defined by the operator, such as by a mouse click on an image of the first sample, at 106. However, the identification of a unique feature may also be done automatically with pattern recognition. The software of the AFM tool may also store the XY coordinate information about the unique feature. Then, a recipe (or pre-defined program or coordinate) for a series of scans or multiple scans at the same or different locations using the unique feature as a landmark can be defined, at 108. An operator may program the recipe via the software or the recipe may already be pre-defined in the software or controller. The recipe may include multiple positions of the sample and multiple iterations of a scan.
Once the recipe is established, the method 100 may execute the defined scans on the sample, at 110. All of the defined scans may be performed; however, in some instances, less than all of the pre-defined scans may be completed, such as upon detection of an error, a malfunction, or a sample misplacement. The types of scans, number of scans, and properties measured may be set by the operator or by software. The scans may provide data to the software to determine features or measurements of the sample. Based on the features or measurements of the part, calibrations, analysis, or other operations may be performed.
Once the scans are done on a first sample, a next sample may be moved into the previously defined sample location marks, at 112, which may be done either manually by the operator or automatically using pattern recognition. The operator may see the position of the next sample on the display, which may also show the sample location marks, the tip location marks, or both. The unique feature may then be identified for the new sample, at 114, which may be done manually, such as by a mouse click by the operator, or automatically using pattern recognition.
The method 100 may then repeat, at 116, executing the defined scans, at 110, for the next part and any parts thereafter. Thus, once a first sample part has been processed with the steps 102-110, the method 100 only need repeat steps 110, 112, and 114 for subsequent sample parts.
Referring to
The method 200 may include manually loading a part onto a system, at 202, such as via a slider or bar with a fixture or carrier. Next, an operator may teach a tip and sample relative location of the first part, at 204. The control monitor may display an image of the part and location markings to allow the operator to position the part. Once the operator has positioned the part, the AFM control computer may generate a crosshair marker, a box, or any combination thereof to indicate on the visual display an ideal tip location, sample location, or both, at 206. The tip location and sample location may be saved to allow placement of successive parts. In some embodiments, the method 200 may be fully automated.
Once the part is approximately in the ideal location, the AFM system may perform a first scan and visually display the scan result on a display, at 208. Then, the operator may use a location selector, such as a mouse click, to define a specific position or unique feature, at 210. The AFM software will then execute any other programmed scans, such as the scans shown at 212 to produce the images. The scans may be used for imaging, measuring, and manipulating matter, especially at the nanoscale.
For a next sample, an operator may return to step 3, at 206, and position the tip location and sample location of the next part in the box or other markings shown on the display. The box or other markings may correspond to the previously saved markings Once the next part is in position, the AFM control computer can execute the rest of the scans. For any next parts, the unique feature may be identified manually, such as by the operator, or automatically using pattern recognition.
In accordance with various embodiments, the methods and systems described herein may be implemented as one or more software programs running on a computer processor or controller. Further, a physical computer readable storage medium may store instructions, that when executed by a processor or computer system, cause the processor or computer system to perform the methods described herein. Dedicated hardware implementations including, but not limited to, application specific integrated circuits, programmable gate arrays, and other hardware devices can likewise be constructed to implement the systems and methods described herein. The systems and methods described herein can be applied to any type of computer processing system that can perform the processes described herein.
The illustrations of the embodiments described herein are intended to provide a general understanding of the structure of the various embodiments. The illustrations are not intended to serve as a complete description of all of the elements and features of apparatus and systems that utilize the structures or methods described herein. Many other embodiments may be apparent to those of skill in the art upon reviewing the disclosure. Other embodiments may be utilized and derived from the disclosure, such that structural and logical substitutions and changes may be made without departing from the scope of the disclosure. Moreover, although specific embodiments have been illustrated and described herein, it should be appreciated that any subsequent arrangement designed to achieve the same or similar purpose may be substituted for the specific embodiments shown.
The present application claims priority to U.S. provisional patent applications Ser. No. 61/512,875, filed Jul. 28, 2011, entitled “Semi-Automatic Scanning Probe Tool”, and Ser. No. 61/521,745, filed Aug. 9, 2011, entitled “Scanning Probe Microscopy Scanning Technique”, the contents of which are hereby incorporated by reference in their entirety.
Number | Name | Date | Kind |
---|---|---|---|
4752686 | Brust | Jun 1988 | A |
5077473 | Elings | Dec 1991 | A |
5400647 | Elings | Mar 1995 | A |
5415027 | Elings | May 1995 | A |
5418363 | Elings | May 1995 | A |
5420796 | Weling | May 1995 | A |
5461907 | Tench | Oct 1995 | A |
5463897 | Prater | Nov 1995 | A |
5496999 | Linker | Mar 1996 | A |
5520769 | Barrett | May 1996 | A |
5553487 | Elings | Sep 1996 | A |
5594845 | Florent | Jan 1997 | A |
5715334 | Peters | Feb 1998 | A |
5757424 | Frederick | May 1998 | A |
5825039 | Hartley | Oct 1998 | A |
5831181 | Majumdar | Nov 1998 | A |
5852232 | Samsavar | Dec 1998 | A |
5898106 | Babcock | Apr 1999 | A |
6057547 | Park | May 2000 | A |
6057914 | Yedur | May 2000 | A |
6172506 | Adderton | Jan 2001 | B1 |
6288392 | Abbott | Sep 2001 | B1 |
6337479 | Kley | Jan 2002 | B1 |
6427345 | Alvis | Aug 2002 | B1 |
6489611 | Aumond | Dec 2002 | B1 |
6590703 | Park | Jul 2003 | B2 |
6651893 | He et al. | Nov 2003 | B2 |
6683316 | Schambler | Jan 2004 | B2 |
6715346 | Shuman | Apr 2004 | B2 |
6752008 | Kley | Jun 2004 | B1 |
6862924 | Xi | Mar 2005 | B2 |
6873867 | Vilsmeier | Mar 2005 | B2 |
6880389 | Hare | Apr 2005 | B2 |
6925860 | Poris | Aug 2005 | B1 |
6975755 | Baumberg | Dec 2005 | B1 |
6980937 | Hayes | Dec 2005 | B2 |
6993959 | Shoelson | Feb 2006 | B2 |
7009172 | Publicover | Mar 2006 | B2 |
7406860 | Zhou | Aug 2008 | B2 |
7435955 | West | Oct 2008 | B2 |
7569077 | Kollin | Aug 2009 | B2 |
7573682 | Pust | Aug 2009 | B2 |
7746404 | Deng | Jun 2010 | B2 |
7770231 | Prater | Aug 2010 | B2 |
8043652 | Eby | Oct 2011 | B2 |
8166567 | Phan | Apr 2012 | B2 |
8296860 | Liu | Oct 2012 | B2 |
8353060 | Watanabe | Jan 2013 | B2 |
8495759 | Wakiyama | Jul 2013 | B2 |
20010038072 | Aumond | Nov 2001 | A1 |
20010054691 | Park et al. | Dec 2001 | A1 |
20020008760 | Nakamura | Jan 2002 | A1 |
20030049381 | Mirkin | Mar 2003 | A1 |
20040134265 | Mancevski | Jul 2004 | A1 |
20070023649 | West | Feb 2007 | A1 |
20070251306 | Zhou et al. | Nov 2007 | A1 |
20080147346 | Eby | Jun 2008 | A1 |
20080308718 | Kollin | Dec 2008 | A1 |
20100031402 | Wakiyama | Feb 2010 | A1 |
20110055982 | Watanabe | Mar 2011 | A1 |
Entry |
---|
Bao Tianming, Automated AFM as an Industrial Process Metrology Tool for Nanoelectronic Manufacturing, Applied Scanning Probe Methods X , NanoScience and Technology, 2008, 359-412, Springer. |
Inspection of Pole Tip Diamondlike Carbon Wear Due to Heater-Induced Head-Disc Contact, Journal of Applied Physics, vol. 99, Issue 8, 2006, Bloomington. |
High Performance Metrology Systems, View Engineering Inc., View Micro-Metrology, 2006, Tempe, AZ. |
Non-Final Office Action, Jun. 24, 2014, U.S. Appl. No. 13/559,034, filed Jul. 26, 2012, Advanced Atomic Force Microscopy Scanning for Obtaining a True Shape, Hiuwen Liu. |
Final Office Action, Feb. 4, 2014, U.S. Appl. No. 13/559,034, filed Jul. 26, 2012, Advanced Atomic Force Microscopy Scanning for Obtaining a True Shape, Hiuwen Liu. |
Zhan, AFM operating-drift detection and analyses based on automated sequential image processing, Nanotechnology, 2007. IEEE-NANO 2007. 7th IEEE Conference on Aug. 2-5, 2007, pp. 748-753 http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4601295&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs—all.jsp%3Farnumber%3D4601295. |
Alex Chen, et al., Enhancement and Recovery in Atomic Force Microscopy Images. |
Babak Mokaberi, et al., Compensation of Scanner Creep and Hysteresis for AFM Nanomanipulation; Apr. 2008. |
B. Mokaberi, et al.;Towards Automatic Nanomanipulation: Drift Compensation in Scanning Probe Microscopes; Conference Apr. 26-May 1, 2004. |
Daniel Y. Abramovitch, et al., A Tutorial on the Mechanisms, Dynamics, and Control of Atomic Force Microscopes; Conference Jul. 9-13, 2007. |
Ambios Technology Corporation Operator's Manual , Q-Scope™ 250/400, Nomad™; Sep. 2007. |
Kindt, Atomic force microscope detector drift compensation by correlation of similar traces acquired at different setpoints; Review of Scientific Instruments vol. 73, No. 6 Jun. 2002. |
West, A Guide to AFM Image Artifacts, Pacific Nanotechnology, Santa Clara CA. http://www.cma.fcen.uba.ar/files/Guide—AFM.pdf. |
Seeger, Surface Reconstruction From AFM and SEM Images, 2004. |
Venkataraman, Automated image analysis of atomic force microscopy images of rotavirus particles, Ultramicroscopy 106 (2006), 829-837. |
Modeling while Interpreting, Overview; Paradigm, 2014. http://www.pdgm.com/Solutions/Interpretation-Modeling/Interpretation-Validation/Interpretation-while-modeling. |
Fantner, Atomic Force Microscopy, Advanced Bioengineering Methods Laboratory, 2013 http://www.eng.uc.edu/˜beaucag/Classes/Characterization/SEM—TEM—Lab/ABML%20%E2%80%93%20AFM%20module%20-%202013.pdf. |
Non-Final Office Action, Oct. 3, 2014, U.S. Appl. No. 13/559,034, filed Jul. 26, 2012, Advanced Atomic Force Microscopy Scanning for Obtaining a True Shape, Hiuwen Liu. |
Number | Date | Country | |
---|---|---|---|
20130031680 A1 | Jan 2013 | US |
Number | Date | Country | |
---|---|---|---|
61512875 | Jul 2011 | US | |
61521745 | Aug 2011 | US |