Cascaded machine learning model for reconstruction of surface topography from light scattering

Light scattering methods are promising for in-process surface measurement. Many researchers have investigated light scattering methods for evaluating surface texture. Researchers working on scatterometry have developed methods to derive surface texture parameters by solving the inverse scattering problem. However, most of the research has been focused only on texture measurement or determination of critical dimensions where feature sizes are less than the wavelength of the light source. In this paper, we propose a new light scattering method to reconstruct the surface topography of grating patterns, using a cascaded machine learning model. The experimental scattering signal can be fed into the machine learning model as the input and the surface topography can be determined as the output. The training dataset, i.e. scattering signals of different surfaces, are generated through a validated rigorous surface scattering model based on a boundary element method (BEM). In this way, the machine learning model can be trained using a big data approach including tens of thousands of datasets, which represent most of the scenarios in real cases. The cascaded machine learning model is designed as a combined top-down, two-layer model implemented using neural networks. The first layer consists of a classification model designed to determine which type of structured surface is being measured, amongst a set of predefined design variants. The second layer contains a regression model, designed to determine the values of the design parameters defining the specific type of structured surface which has been identified, for example the nominal pitch and height of its periodic features. We have developed a prototype system and conducted experiments to verify the proposed method. Structured surfaces containing grating patterns were considered, and different types of gratings were analysed. The results were validated by comparison with measurements performed with atomic force microscopy

Optical difference engine for defect inspection in highly-parallel manufacturing processes

Traditional defect inspection for highly-parallel manufacturing processes requires the processing of large measurement datasets, which is often not fast enough for in-situ inspection of large areas with high resolution. This study develops an all-optical difference engine for fast defect detection in highly-parallel manufacturing processes, where the detection of defects (differences from nominal) is performed optically and in real-time. Identification of defects is achieved through an optical Fourier transform and spatial filtering, detecting differences between two real objects by nulling information that is repeated in each object. The developed prototype device is demonstrated using geometric patterns of similar scale to components in printed electronic circuit

A Gaussian process and image registration based stitching method for high dynamic range measurement of precision surfaces

Optical instruments are widely used for precision surface measurement. However, the dynamic range of optical instruments, in terms of measurement area and resolution, is limited by the characteristics of the imaging and the detection systems. If a large area with a high resolution is required, multiple measurements need to be conducted and the resulting datasets needs to be stitched together. Traditional stitching methods use six degrees of freedom for the registration of the overlapped regions, which can result in high computational complexity. Moreover, measurement error increases with increasing measurement data. In this paper, a stitching method, based on a Gaussian process, image registration and edge intensity data fusion, is presented. Firstly, the stitched datasets are modelled by using a Gaussian process so as to determine the mean of each stitched tile. Secondly, the datasets are projected to a base plane. In this way, the three-dimensional datasets are transformed to two-dimensional (2D) images. The images are registered by using an (x, y) translation to simplify the complexity. By using a high precision linear stage that is integral to the measurement instrument, the rotational error becomes insignificant and the cumulative rotational error can be eliminated. The translational error can be compensated by the image registration process. The z direction registration is performed by a least-squares error algorithm and the (x, y, z) translational information is determined. Finally, the overlapped regions of the measurement datasets are fused together by the edge intensity data fusion method. As a result, a large measurement area with a high resolution is obtained. A simulated and an actual measurement with a coherence scanning interferometer have been conducted to verify the proposed method. The stitching result shows that the proposed method is technically feasible for large area surface measurement

On-machine surface defect detection using light scattering and deep learning

This paper presents an on-machine surface defect detection system using light scattering and deep learning. A supervised deep learning model is used to mine the information related to defects from light scattering patterns. A convolutional neural network is trained on a large dataset of scattering patterns that are predicted by a rigorous forward scattering model. The model is valid for any surface topography with homogeneous materials and has been verified by comparing with experimental data. Once the neural network is trained, it allows for fast, accurate and robust defect detection. The system capability is validated on micro-structured surfaces produced by ultra-precision diamond machining

Fabrication of agar-based tissue-mimicking phantom for the technical evaluation of biomedical optical imaging systems

The development process of the optical systems for various biomedical applications typically involve evaluations of technical performance. One popular evaluation method is to use a reference object such as a phantom that exhibits similar optical properties of tissue. Fabrication of a consistent phantom with known optical properties, such as scattering and absorption, is essential for accurate technical evaluation of the optical system. This paper presents a protocol for fabricating an agar-based tissue-mimicking phantom, offering practical guidance to ensure consistent and reproducible phantom creation. In addition, optical setups that measure light information required for quantifying the optical properties via an inverse adding-doubling (IAD) method are discussed. We demonstrated the fabrication of phantoms with diverse scattering and absorption properties, and the IAD method successfully quantified the optical properties. Moreover, we employed the phantom to assess the imaging depth limitation of a hyperspectral imaging system, demonstrating potential usage of phantoms for performing technical evaluation.</p

Measurement of laser powder bed fusion surfaces with light scattering and unsupervised machine learning

Quality monitoring for laser powder bed fusion (L-PBF), particularly in-process and real-time monitoring, is of importance for part quality assurance and manufacturing cost reduction. Measurement of layer surface topography is critical for quality monitoring, as any anomaly on layer surfaces can result in defects in the final part. In this paper, we propose a surface measurement method, based on the use of scattered light patterns and a convolutional autoencoder-based unsupervised machine learning method, designed and trained using a large set of scattering patterns simulated from reference surfaces using a scattering model. The advantage of using an autoencoder is that the monitoring model can be trained using solely data from acceptable surfaces, without the need to ensure the presence of representative observations for all the types of possible surface defects. The advantage of using simulated data for training is that we can obtain an effective monitoring solution without the need for a large collection of experimental observations. Here we report the results of a preliminary investigation on the performance of the proposed solution, where the trained autoencoder is tested on experimental data obtained off-process, using a dedicated experimental apparatus for generating and collecting light scattering patterns from manufactured L-PBF surfaces. Our results indicate that the proposed monitoring solution is capable of detecting both acceptable and anomalous surfaces. Although further validation is required to fully assess performance within an on-machine and in-process setup, our preliminary results are encouraging and provide a glimpse of the potential benefits of using our surface measurement solution for L-PBF in-process monitoring

Fabrication of agar-based tissue-mimicking phantom for the technical evaluation of biomedical optical imaging systems

The development process of the optical systems for various biomedical applications typically involve evaluations of technical performance. One popular evaluation method is to use a reference object such as a phantom that exhibits similar optical properties of tissue. Fabrication of a consistent phantom with known optical properties, such as scattering and absorption, is essential for accurate technical evaluation of the optical system. This paper presents a protocol for fabricating an agar-based tissue-mimicking phantom, offering practical guidance to ensure consistent and reproducible phantom creation. In addition, optical setups that measure light information required for quantifying the optical properties via an inverse adding-doubling (IAD) method are discussed. We demonstrated the fabrication of phantoms with diverse scattering and absorption properties, and the IAD method successfully quantified the optical properties. Moreover, we employed the phantom to assess the imaging depth limitation of a hyperspectral imaging system, demonstrating potential usage of phantoms for performing technical evaluation.</p

Lens aberration compensation in interference microscopy

Emergence of products that feature functional surfaces with complex geometries, such as freeform optics in consumer electronics and augmented reality and virtual reality, requires high-accuracy non-contact surface measurement. However, large discrepancies are often observed between the measurement results of optical methods and contact stylus methods, especially for complex surfaces. For interference microscopy, such as coherence scanning interferometry, the three-dimensional surface transfer function provides information about the instrument spatial frequency passband and about lens aberrations that can result in measurement errors. Characterisation and phase inversion of the instrument’s three-dimensional surface transfer function yields an inverse filter that can be applied directly to the three-dimensional fringe data. The inverse filtering is shown to reduce measurement errors without using any data processing or requiring any a priori knowledge of the surface. We present an experimental verification of the characterisation and correction process for measurements of several freeform surfaces and an additive manufactured surface. Corrected coherence scanning interferometry measurements agree with traceable contact stylus measurements to the order of 10 nm

Pyrolysis gas as a carbon source for biogas production via anaerobic digestion

Carbon is an important resource for anaerobes to enhance biogas production. In this study, the possibility of using simulated pyrolysis gas (SPG) as a carbon source for biogas production was investigated. The effects of stirring speed (SS), gas holding time (GHT), and H2 addition on biomethanation of SPG were evaluated. The diversity and structure of microbial communities were also analyzed under an illumina MiSeq platform. Results indicated that at a GHT of 14 h and an SS at 400 rpm, SPG with up to 64.7% CH4could be bio-upgraded to biogas. Gas–liquid mass transfer is the limitation for SPG biomethanation. For the first time, it has been noticed that the addition of H2 can bioupgrade SPG to high quality biogas (with 91.1% CH4). Methanobacterium was considered as a key factor in all reactors. This study provides an idea and alternative way to convert lignocellulosic biomass and solid organic waste into energy (e.g., pyrolysis was used as a pretreatment to produce pyrolysis gas from biomass, and then, pyrolysis gas was bioupgraded to higher quality biogas via anaerobic digestion)