Abstract

Automatic nanomanipulation and nanofabrication with an Atomic Force Microscope (AFM) is a precursor for nanomanufacturing. In ambient conditions without stringent environmental controls, nanomanipulation tasks require extensive human intervention to compensate for the many spatial uncertainties of the AFM. Among these uncertainties, thermal drift is especially hard to solve because it tends to increase with time and cannot be compensated simultaneously by feedback. In this paper, an automatic compensation scheme is introduced to measure and estimate drift. This information can be subsequently utilized to compensate for the thermal drift so that a real-time controller for nanomanipulation can be designed as if drift does not exist. Experimental results show that the proposed compensation scheme can predict drift with a small error. Future work is aimed at reducing the error even further through temperature feedback. Keywords - nanomanipulation, Atomic Force microscope, drift, Phase-Correlation Method, Neural Network

Meeting Name

5th IEEE Conference on Nanotechnology (2005: July 11--15, Nagoya, Japan)

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Third Department

Chemistry

Keywords and Phrases

Atomic Force Microscope (AFM); Neural Network; Phase-Correlation Method; Drift; Nanomanipulation; Nanomanufacturing

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.