The presented paper shows the concept and optical design of an array-type Mirau-based OCT system for early diagnosis of skin cancer. The basic concept of the sensor is a full-field, full-range optical coherence tomography (OCT) sensor. The micro-optical interferometer array in Mirau configuration is a key element of the system allowing parallel imaging of multiple field of views (FOV). The optical design focuses on the imaging performance of a single channel of the interferometer array and the illumination design of the array. In addition a straylight analysis of this array sensor is given.
Microoptical components play an increasing role in different technology fields such as medical engineering, materials
and information processing, imaging and metrology. But their realization needs the combination of modern design
concepts with sophisticated processing technologies, new materials and design tools. Furthermore, the introduction of
ambitious processing technologies must be accompanied by effective metrology and inspection tools. Therefore, this
paper reports about the technologies for making microoptics at ITO. Because sophisticated measurement tools are an
indispensable part of the fabrication process, the paper describes our multi-scale inspection approach for the testing of
microstructures on wafer-scale level. Finally, some representative applications of microoptical components for advanced
measurement and imaging are explained.
The hybrid measurement principle Chromatic Confocal Spectral Interferometry combines Spectral Interferometry
with Chromatic Confocal Microscopy and therefore benefits from their respective advantages. Our actual
demonstrator setup enables an axial measurement range up to 100 μm with resolution up to 5 nm depending on
the employed evaluation method and the characteristics of the object’s surface. On structured surfaces, lateral
features down to 1 μm can be measured. As the sensor raw signal consists of a Spectral Interferometry type
wavelet modulated by a confocal envelope, two classes of evaluation methods working on the phasing or the
position of the envelope are employed. Even though both of these information channels are subject to their
respective problems, we show that a proper combination of the individual methods leads to a robust signal
evaluation. In particular, we show that typical artifacts on curved surfaces, that are known from Chromatic
Confocal Microscopy, are minimized or completely removed by taking the phasing of the Spectral Interferometry
wavelet into consideration. At the same time the problem of determining the right fringe order of the Spectral
Interferometry signal at surface discontinuities can be solved by evaluation of the confocal envelope. We present
here a first approach using a contrast threshold on the signal and a median referencing for trusted sections of the
analysed topography, which yields a reduction of artifacts in a submicron range on steep gradients, discontinuous
specimen or curved mirror-like surfaces.
Considering modern manufacturing processes, there is an increasing demand for flexible, fast and precise inspection
systems. Usually, the holistic inspection of technical components with a complex three-dimensional surface, like gears,
needs to be separated into inspection steps. Different areas on the object need to be verified with respect to varying characteristic
specifications, e.g. related to defects or roughness properties. Such manifold inspection tasks can for instance
be realized using a multi-sensor measurement system which is also equipped with a multi-axis system to optimally move
and rotate each sensor with respect to any desired position at the object’s surface. In order to generate an automatic inspection
system, the entire process is defined with respect to a polygonal model of the measurement specimen, such that
different sub-regions are connected with different specifications and parameterizations that this region must meet and
hence needs to be verified by the inspection system. However, the data acquisition with respect to sub-regions on the
model’s surface and the integration of obtained datasets in the model’s coordinate system is only feasible if the transformation
of the real object to the model is determined before. Consequently, this needs to be determined in the initialization
phase of the overall inspection process.
Confocal sensors are well established in optical surface metrology and their performance has been thoroughly studied
both experimentally and theoretically. However most of the theoretical work has been based upon the assumption of
locally flat or point like measurement objects. As confocal sensors have become increasingly popular in industrial
inspection of rough surfaces in recent years, severe measurement artifacts have been observed in certain situations. The
physical reason for these artifacts was not fully understood and therefore a systematic procedure to choose a set of sensor
parameters, that minimizes the impact of these artifacts, has been missing. In fact planning measurements of rough
surfaces has been a formidable task that even highly experienced experts approached on a trial and error basis. To make
things even worse, different confocal measurement systems, e.g. from different manufacturers, and different sensor
parameters, e.g. different numerical aperture objectives, typically give substantially differing results. A reliable
interpretation of these results let alone a sound judgement of the remaining uncertainty in the measurement results is very
difficult. Starting from a quick review of a recently developed signal model, we therefore present an attempt to
systematically guide the user of confocal sensors through the planning of an inspection task. In order to support our
proposal, we present measurements of two roughness calibration standards, that were conducted with varying numerical
aperture objectives on a custom build confocal microscope with rotating micro lenses. The uncertainty in these
measurements is then compared to the predictions of our assistance system.
Ray tracing still is the workhorse in optical design and simulation. Its basic principle, propagating light as a set of mutually independent rays, implies a linear dependency of the computational effort and the number of rays involved in the problem. At the same time, the mutual independence of the light rays bears a huge potential for parallelization of the computational load. This potential has recently been recognized in the visualization community, where graphics processing unit (GPU)-accelerated ray tracing is used to render photorealistic images. However, precision requirements in optical simulation are substantially higher than in visualization, and therefore performance results known from visualization cannot be expected to transfer to optical simulation one-to-one. In this contribution, we present an open-source implementation of a GPU-accelerated ray tracer, based on nVidias acceleration engine OptiX, that traces in double precision and exploits the massively parallel architecture of modern graphics cards. We compare its performance to a CPU-based tracer that has been developed in parallel.
Confocal sensors are well established in optical surface metrology. Especially when measuring rough surfaces, their robustness is widely appreciated. However, it was shown lately that certain object features can produce severe artifacts in confocal measurements that are hard to identify as false measurements. Experimental evidence of these artifacts is given with a measurement of a suitable surface conducted with a chromatic confocal point sensor. Furthermore various simulations are presented that identify a self-imaging property of the surface features as the root of the artifacts. These simulations also pave the way to a more precise yet still intuitive signal model for confocal measurements.
In recent years image-processing has become a central part of optical inspection and measurement systems. Typically, after measuring the given specimen by utilizing a suitable sensor, image-processing algorithms are used to detect dedicated features such as surface defects. These algorithms are usually designed, optimized, and tested by an image-processing expert according to the task specifications. A methodology (based on genetic programming) is presented to automatically generate, optimize, and test such algorithms without the necessity of an image-processing expert. We also present several examples of inspection tasks to support the concept. For efficiency, an automated multi-scale multi-sensor inspection strategy is employed.
In this paper, we demonstrate how short coherence digital holography with a pulsed fiber laser frequency comb may be
used for multi-level optical sectioning. For the proof of the principle, a conic object having a size of few centimeters is
used. The object shape is obtained by digitally reconstructing and processing a sequence of holograms recorded during
stepwise shifting of a spherical mirror in the reference arm of the holographic set-up. First experimental results are
presented.
Multi scale systems offer the opportunity to balance the conflict between execution time, measurement volume and resolution
for the inspection of highly complex surface profiles. An example of such a task is the inspection of gears. At first,
the coarse position and form of the specimen is registered by a sensor measuring with comparatively low resolution but a
large field of view. Possible defects near to the resolution limit are indicated and new regions of interest for higher resolved
measurements are identified. As prerequisite for a successful multi-scale inspection, every sampled data set, acquired
in different scales and at varying positions, must be registered in one global data model. This is only possible if
the extrinsic coordinate transform from the sensor's internal coordinate system to the common, global coordinate system
of the inspected object and its uncertainties are known. In this paper, we present an approach for the extrinsic calibration
using the example of a multi-zoom fringe projection sensor mounted on a multi-axes measurement system. Finally we
show the measurement result of a gear, where several sampled patches are merged together into one point cloud with the
aid of the presented calibration.
In manufacturing monitoring and inspection is an essential task to maintain a high product quality. Therefore a variety of
systems (e.g. tactile systems, acoustic systems, optical systems,...) is used. However there is still a lack in controlling the
product quality near the production machine. For the selection and the design of an appropriate monitoring strategy the
specification of the applied sensors is of crucial importance. Optical sensors are in general suitable to measure quality
relevant features. But they are often not robust enough, to use them in harsh environments such as the workshop floor.
However to detect as early as possible if quality runs out off specification, the high resolution of optical measurement
systems is often not needed. In these cases optical sensors can be implemented successfully even if their measurement
uncertainty is increasing due to the harsh environment. To verify this hypothesis an evaluation of environmental
influences has to be made and a comparison between the acceptable and still achieveable measurement uncertainty has to
be made.
For this reason a conceptual consideration regarding optical sensors developed for in-process monitoring is presented.
The focus will be on the investigation of the influence of the environment on the measurement result, and on strategies
how these can be estimated. Based on this an appropriate design and construction of the sensor system can be obtained.
An automatic method for the positioning of a test surface in a non-null interferometer is presented. If the test surface is
positioned incorrectly with respect to the test beam this leads to aberrations, which distort the measurement of the
surface. A central issue in the interferometric characterization of surfaces is to avoid aberrations due to an incorrect
placement of the test surface. In case of spherical and plane surfaces these errors can usually be distinguished from the
surface figure errors and are eliminated in post processing. For aspheric and free-form surfaces this task is no longer
trivial. Therefore it is important to minimize the alignment error of the surface. In this work the effect on the measured
phase due to lateral and axial displacements of the aspheric surface is calculated and an adjustment method for the
positioning of the surface at a predefined measurement location is presented. Experimental results showing the feasibility
of the proposed procedure are presented.
Simulation of grating spectrometers constitutes the problem of propagating a spectrally broad light field through a
macroscopic optical system that contains a nanostructured grating surface. The interest of the simulation is to quantify
and optimize the stray light behaviour, which is the limiting factor in modern high end spectrometers. In order to
accomplish this we present a simulation scheme that combines a RCWA (rigorous coupled wave analysis) simulation of
the grating surface with a selfmade GPU (graphics processor unit) accelerated nonsequential raytracer. Using this, we are
able to represent the broad spectrum of the light field as a superposition of many monochromatic raysets and handle the
huge raynumber in reasonable time.
Optical inspection systems constitute hardware components (e.g. measurement sensors, lighting systems, positioning
systems etc.) and software components (system calibration techniques, image processing algorithms for defect detection
and classification, data fusion, etc.). Given an inspection task choosing the most suitable components is not a trivial
process and requires expert knowledge. For multiscale measurement systems, the optimization of the measurement
system is an unsolved problem even for human experts. In this contribution we propose two assistant systems (hardware
assistant and software assistant), which help in choosing the most suitable components depending on the task considering
the properties of the object (e.g. material, surface roughness, etc.) and the defects (e.g. defect types, dimensions, etc.).
The hardware assistant system uses general rules of thumb, sensor models/simulations and stored expert knowledge to
specify the sensors along with their parameters and the hierarchy (if necessary) in a multiscale measurement system. The
software assistant system then simulates many measurements with all possible defect types for the chosen sensors.
Artificial neural networks (ANN) are used for pre-selection and genetic algorithms are used for finer selection of the
defect detection algorithms along with their optimized parameters. In this contribution we will show the general
architecture of the assistant system and results obtained for the detection of typical defects on technical surfaces in the
micro-scale using a multiscale measurement system.
Efficient inspection of an object for deformations and defects requires comparison with an existing real or simulated reference model. Fourier descriptor (FDs) based shape analysis is an effective method for describing a shape using the Fourier transform. This shape representation can be easily modified to achieve shift, rotation, and scale invariance. We propose two new methods, namely the ring sampling and the spiral sampling methods, which enable the usage of FDs in order to detect defects on micro-optical elements like microlens arrays. As an example the measurement data obtained from a confocal microscope has been used to show the effectiveness of the two approaches for both indicating and detecting surface defects. Microlens arrays with different types of defects including global (deformed lenses causing aberrations) and local defects (scratches) were simulated using a confocal microscopy simulation tool to test the reliability of the methods. A classifier differentiates between global and local defective lenses. In order to represent other kinds of objects using FDs, the methods can be easily modified or extended. The whole process has been implemented into an automated multiscale multisensor measurement system, which focuses on fast detection of defects on micro-optical and microelectromechanical systems.
Optical metrology has shown to be a versatile tool for the solution of many inspection problems. The main advantages of
optical methods are the noncontact nature, the non-destructive and fieldwise working principle, the fast response, high
sensitivity, resolution and accuracy. Consequently, optical principles are increasingly being considered in all steps of the
evolution of modern products. However, the step out of the laboratory into the harsh environment of the factory floor
was and is a big challenge for optical metrology. The advantages mentioned above must be paid often with strict
requirements concerning the measurement conditions and the object under test. For instance, the request for
interferometric precision in general needs an environment where high stability is guaranteed. If this cannot be satisfied to
a great extent special measures have to be taken or compromises have to be accepted. But the rapid technological
development of the components that are used for creating modern optical measurement systems, the unrestrained growth
of the computing power and the implementation of new measurement and inspection strategies give cause for optimism
and show that the high potential of optical metrology is far from being fully utilized. In this article current challenges to
optical metrology are discussed and new technical improvements that help to overcome existing restrictions are treated.
On example of selected applications the progress in bringing optical metrology to the real world is shown.
Surface metrology of MEMS requires high resolution sensors due to their fine structures. An automated multiscale
measurement system with multiple sensors at multiple scales enables fast acquisition of the surface data by utilizing high
resolution sensors only at locations required. We propose a technique that depends on the fact that often MEMS have
features (e.g. combs) repeating across the surface. These features can be segmented and fused to generate an ideal
template. We present an automated similarity search approach based on feature detection, rotation invariant matching,
and sum of absolute differences to find similar structures on the specimen. Then, similar segments are fused and replaced
in the original image to generate an ideal template.
An automatic method for the positioning of the test surface in a non-null interferometer is presented. A major task in the interferometric testing of surfaces is to avoid the introduction of surface aberrations due to an incorrect placement of the test object in the interferometer cavity. In the case of plane and spherical surfaces, adjustment errors can usually be distinguished from surface figure errors and therefore removed, but in the case of aspherical surfaces this task becomes nontrivial. In this work, the effect on the measured phase due to lateral and axial displacements of the aspherical surface is calculated, and an adjustment method for the positioning of the surface at a predefined measurement location presented. Experimental results showing the feasibility of the proposed procedure are shown.
Optical inspection using multi-sensor multi-scale systems requires the selection of proper sensors, their parameters
(e.g. resolution, N.A, lighting conditions), and measurement strategies. We propose an assistance system that
automatically selects the suitable sensors and their parameters for an inspection specification. The specimen and
the defects are described based on their properties (e.g. geometry, material etc) to the assistance system. The
system then uses different "sub-assistants", each designed for a specific measurement technique, to recommend the
most suitable measurement setups. The system and initial results for fringe projection techniques are presented.
In former publications we presented an automated multiscale measurement system (AMMS) based on an adaptable
active exploration strategy. The system is armed with several sensors linked by indicator algorithms to identify
unresolved defects and to trigger finer resolved measurements. The advantage of this strategy in comparison to single
sensor approaches is its high flexibility which is used to balance the conflict between measurement range, resolution and
duration. For an initial proof of principle we used the system for inspection of microlens arrays.
An even higher challenge for inspection systems are modern micro electro-mechanical systems (MEMS). MEMS consist
of critical functional components which range from several millimeters down to micrometers and typically have
tolerances in sub-micron scale. This contribution is focused on the inspection of MEMS using the example of micro
calibration devices. This new class of objects has completely different surface characteristics and features hence it is
necessary to adapted the components of the AMMS. Typical defects found on calibration devices are for example broken
actuator combs and springs, surface cracks or missing features. These defects have less influence on the optical
properties of the surface and the MEMS surface generates more complex intensity distributions in comparison
microlense arrays. At the same time, the surface features of the MEMS have a higher variety and less periodicity which
reduce the performance of currently used algorithms. To meet these requirements, we present new indicator algorithms
for the automated analysis of confocal as well as conventional imaging data and show initial multiscale inspection
results.
To increase the quality of future products and decrease the manufacturing cost at the same time a systematic control of
the fabricated objects is necessary. A promising approach for inline quality control of surface and form parameters is the
use of optical measurement systems. This is due to the non-destructive nature of the optical measurement techniques. But
in the production environment there are many challenges to overcome for optical sensors. Examples are temperature
fluctuation, vibrations, fluids on the object surface and rough surfaces. Therefore, it is likely that not all optical
measurement methods are suitable for that task. Hence, a classification of the different principles is necessary with the
objective to identify the most appropriate measurement approach for a particular inspection task. In this contribution we
start with a systematic approach for a review of sensors within production systems. Then we concentrate on the most
robust class of optical sensors, the point sensors. In order to minimize the effect of mechanical vibrations it is desirable
to employ measurement techniques that are able to measure the height of an object point in a very short time. Therefore,
we focus in this work on chromatic-confocal microscopy and spectral interferometry. The aim is to compare these
measurement methods for their ability to cope with the challenges given by the production environment in general. To
this end we will develop simulation models for the mentioned techniques and compare two exemplarily sensors for their
capability to be used for process control.
Chromatic confocal spectral interferometry (CCSI) is a hybrid method for fast topography measurement, which
combines the advantages of the interferometric gain and accuracy with the robustness of confocal microscopy. The
CCSI-principle provides a single shot measurement of depth while offering a higher lateral resolution than commonly
used spectral interferometers. This contribution is focused on the modeling and simulation of a CCSI-sensor for
measuring rough surfaces, based on sequential and non-sequential ray-tracing. With the simulation, the influence of
surface roughness, surface reflectivity, and surface contamination on reliability of the sensor can be estimated.
Multi-scale measurement systems utilise multiple sensors which differ in resolution and measurement field to pursue an
active exploration strategy. The different sensor scales are linked by indicator algorithms for further measurement
initiation. A major advantage of this strategy is a reduction of the conflict between resolution, time and field. This
reduction is achieved by task specific conditioning of sensors, indicator algorithms and actuators using suitable
uncertainty models. This contribution is focused on uncertainty models of sensors and actuators using the example of a
prototype multi-scale measurement system. The influence of the sensor parameters, object characteristics and
measurement conditions on the measurement reliability is investigated exemplary for the middle-scale sensor, a confocal
microscope.
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