A traceable calibration setup for investigation of the quasi-static and the dynamic performance of nano-positioning
stages is detailed, which utilizes a differential plane-mirror interferometer with double-pass configuration from the
National Physical Laboratory (NPL). An NPL-developed FPGA-based interferometric data acquisition and decoding
system has been used to enable traceable quasi-static calibration of nano-positioning stages with high resolution. A lockin
based modulation technique is further introduced to quantitatively calibrate the dynamic response of moving stages
with a bandwidth up to 100 kHz and picometer resolution. First experimental results have proven that the calibration
setup can achieve under nearly open-air conditions a noise floor lower than 10 pm/sqrt(Hz). A pico-positioning stage,
that is used for nanoindentation with indentation depths down to a few picometers, has been characterized with this
The growth in nanotechnology has led to an increased requirement for traceable dimensional measurements of
nanometre-sized objects and micrometre-sized objects with nanometre tolerances. To meet this challenge NPL has
developed both purpose built instrumentation and added metrology to commercially available equipment. This paper
describes the development and use of a selection of these instruments that include: atomic force microscopy, x-ray
interferometry, a low force balance, a micro coordinate measuring machine and an areal surface texture measuring
We have recently described a technique for optical line-width measurements. The system currently is capable of
measuring line-width down to 60 nm with a precision of 2 nm, and potentially should be able to measure down to 10nm.
The system consists of an ultra-stable interferometer and artificial neural networks (ANNs). The former is used to
generate optical profiles which are input to the ANNs. The outputs of the ANNs are the desired sample parameters.
Different types of samples have been tested with equally impressive results. In this paper we will discuss the factors that
are essential to extend the application of the technique. Two of the factors are signal conditioning and sample
classification. Methods, including principal component analysis, that are capable of performing these tasks will be
In this paper, we will describe a technique that combines a common path scanning optical interferometer with artificial
neural networks (ANN), to perform track width measurements that are significantly beyond the capability of
conventional optical systems.
Artificial neural networks have been used for many different applications. In the present case, ANNs are trained using
profiles of known samples obtained from the scanning interferometer. They are then applied to tracks that have not
previously been exposed to the networks. This paper will discuss the impacts of various ANN configurations, and the
processing of the input signal on the training of the network.
The profiles of the samples, which are used as the inputs to the ANNs, are obtained with a common path scanning
optical interferometer. It provides extremely repeatable measurements, with very high signal to noise ratio, both are
essential for the working of the ANNs. The characteristics of the system will be described.
A number of samples with line widths ranging from 60nm-3μm have been measured to test the system. The system can
measure line widths down to 60nm with a standard deviation of 3nm using optical wavelength of 633nm and a system
numerical aperture of 0.3. These results will be presented in detail along with a discussion of the potential of this