This paper proposes AI-based solutions for optimizing semiconductor manufacturing processes.
problem Optimizing semiconductor manufacturing processes with advanced analytics.
method Evolutionary Computing and Deep Learning algorithms for feature selection and neural networks.
result Advanced algorithm for intelligent feature selection in semiconductor manufacturing.
MFRL-BI controls manufacturing processes without needing accurate models.
problem Model inaccuracies in complex manufacturing systems.
method Model-free reinforcement learning with Bayesian inference.
result Demonstrated to perform well in a nonlinear CMP process.
New method uses SEMs to uncover cause-effect in manufacturing processes.
problem Complex cause-and-effect relationships in manufacturing processes.
method Using Structural Equation Models with non-linear relationships.
result More informative cause-effect relationships derived from data.
Paper introduces a hybrid GPR model for more interpretable RUL prediction in aeroengine.
problem Challenges in interpreting and modeling uncertainty in RUL prediction models.
method Modified Gaussian Process Regression (GPR) with temporal feature extraction.
result Effective prediction of RUL intervals with transparent feature significance.
Framework optimizes expensive manufacturing processes efficiently.
problem Optimizing input parameters for advanced manufacturing methods.
method Bayesian optimization with tailored acquisition function and parallel acquisition.
result Framework efficiently finds optimal parameters with minimal process cost.
Paper proposes tensor-based method for semiconductor manufacturing process control.
problem Challenges of traditional process control methods in high-dimensional image-based overlay errors.
method Builds a high-dimensional process model, proposes tensor-on-vector regression algorithms, designs EWMA controller for tensor data.
result The method reduces overlay errors using limited control recipes and is superior especially when disturbances are not stable.
Automated visual inspection in the semiconductor industry aims to detect and classify manufacturing defects utilizing modern image processing techniques. While an earliest possible detection of defect patterns allows quality control and automation of manufacturing chains, manufacturers benefit from an increased yield a…
Study proposes explainable analytics for manufacturing process planning.
problem Improving data-driven decision-making in manufacturing.
method Combines process mining, machine learning, and XAI. Uses deep learning for prediction and Shapley values/ICE plots for explanations.
result Enhanced decision-making capabilities through local post-hoc explanations.
A new framework optimizes manufacturing decisions with less data and time.
problem Optimizing complex systems with multiple conflicting objectives.
method Data-driven Bayesian optimization using sequential learning.
result The proposed algorithm achieves the actual Pareto front with less data.
We present a novel unsupervised deep learning approach that utilizes the encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed not only to detect whether there exists an anomaly at a given time step, but also to predict what wi…
Paper uses CNN to predict process parameters from molten pool data in WLAM.
problem Achieving high quality in WLAM through real-time process control.
method Proposes a multi-modality CNN architecture to predict process parameters from molten pool sensor data.
result Improved prediction performance of multi-modal CNN compared to uni-modal approach.
Framework predicts melt pool geometry with AI, improving manufacturing quality.
problem Achieving consistent product quality in Metal Additive Manufacturing.
method Surprise-guided sequential learning framework integrating CTGAN for limited data.
result Enhanced predictive accuracy for melt pool dimensions.
This work develops a fast-running ROM for MOOSE-based AM model using OL.
problem Achieving desired material properties in real-time manufacturing processes.
method Operator learning (OL) and Fourier neural operator for ROM development.
result OL-based ROM outperforms conventional deep neural network-based ROM in benchmark tests.
Paper benchmarks machine learning for detecting process curve drifts.
problem Detecting drifts in multivariate manufacturing process data.
method Synthetic data generation and evaluation score introduction.
result Existing algorithms often fail with complex drift scenarios.
The paper uses machine learning to optimize rework policies in semiconductor manufacturing.
problem Optimizing rework steps to increase yield without increasing costs.
method Applied double/debiased machine learning (DML) to estimate treatment effects.
result Derived optimal rework policies and estimated their value empirically.
Study compares deterministic and probabilistic ML for precise AM component dimensions.
problem Accurately estimate dimensions of additively manufactured parts with variability.
method Employed models integrating continuous and categorical factors, tested deterministic and probabilistic ML methods.
result Gaussian Process Regression and Bayesian Neural Networks provide strong predictive performance and uncertainty quantification.
Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from a computer-aided design model by successively adding material layer by layer. AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing as well as manufacturing functional p…
Unified framework for efficient surrogate modeling in manufacturing.
problem Large data requirements and heterogeneous data sources in manufacturing.
method Hierarchical multi-task multi-fidelity (H-MT-MF) framework for Gaussian process-based surrogate modeling.
result Improves prediction accuracy by up to 23% compared to existing methods.
Develops a framework for continual learning in anomaly detection.
problem Deterioration of monitoring performance due to new defect categories.
method Pseudo replay-based class incremental learning with oversampling.
result Enhanced monitoring performance and flexibility in model architecture.
3D Convolutional Neural Networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object. However, interpreting the decision making process of these 3D-CNNs is still an infeasible task. In this paper, we present a unique 3D-CNN based Gradient-weighted Class Activation Mapping method (3D…
The paper uses causal machine learning to optimize rework decisions in manufacturing.
problem Optimizing rework policies in manufacturing systems to balance yield improvement and rework costs.
method Proposes a causal model using double/debiased machine learning (DML) techniques to estimate conditional treatment effects and derive rework policies.
result Achieved a yield improvement of 2-3% during the color-conversion process of white LEDs.
New method optimizes processes under constraints using bivariate Gaussian models.
problem Optimizing processes with constraints using traditional methods.
method Developed a constrained expected improvement acquisition function using bivariate Gaussian process models.
result Demonstrated improved performance in a manufacturing cure process optimization.
In computer chip manufacturing, the study of etch patterns on silicon wafers, or metrology, occurs on the nano-scale and is therefore subject to large variation from small, yet significant, perturbations in the manufacturing environment. An enormous amount of information can be gathered from a single etch process, a se…
Generates synthetic manufacturing data for causal discovery benchmarking.
problem Lack of suitable real data for validating causal discovery algorithms.
method Distributional random forests for estimating conditional distributions.
result Semisynthetic manufacturing data adheres to a causal model.
Deep CNN monitors AM quality with high accuracy.
problem Quality control in AM processes.
method Deep Convolutional Neural Network (CNN) model trained and tested online.
result 94% accuracy and 96% specificity in classifying AM quality.
This work develops fast and accurate ROMs for AM models using OL methods.
problem Achieving specific material properties in AM by manipulating process parameters increases computational load.
method Operator learning (OL) approach with Fourier neural operator (FNO) and DeepONet.
result OL methods offer comparable performance and outperform DNN in accuracy and generalizability.
A RL approach optimizes metal AM process parameters for consistent melt pool depth.
problem Optimizing process parameters for metal additive manufacturing to ensure repeatability and control microstructure.
method A Reinforcement Learning (RL) framework based on Q-learning is applied to find optimal laser power and scan velocity combinations.
result The RL framework learns optimal process parameters without prior knowledge, providing a model-free approach.
Proposes a model for predicting events from event streams.
problem Predicting events like part replacement and failure in manufacturing and teleservice systems.
method Non-parametric prognostic framework using MGCP modulated Poisson processes.
result MGCP prior facilitates sharing of information and analysis of flexible event patterns.
About the economic growth the Keynesian theorists defend circular and cumulative processes, benefiting the rich localities and harming the poorest, without external interventions. In these processes the Verdoorn law has an important role. For Verdoorn (1949) the productivity growth rate is endogenous and depends of the…
Deep neural nets classify tool wear in blanking processes.
problem Predicting tool wear in blanking processes to improve quality and reduce downtime.
method Deep Convolutional Neural Networks trained on workpiece images of 16 wear states.
result High accuracy in predicting tool wear states.
The manufacturing sector is envisioned to be heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring …
This paper evaluates data enrichment techniques for rare event detection in manufacturing.
problem Rare events in manufacturing lead to unplanned downtime and high energy consumption.
method Time series data augmentation, sampling, and imputation techniques combined with supervised machine learning.
result Data enrichment enhances rare failure event detection and prediction by up to 48%.
Develops framework for understanding deep learning in time series data.
problem Understanding and explaining decisions made by deep learning models in time series data.
method Uses deep neural networks to capture and explain temporal dependencies in time series data.
result Framework successfully captures and explains temporal dependencies in various synthetic and real-world datasets.
In processing and manufacturing industries, there has been a large push to produce higher quality products and ensure maximum efficiency of processes. This requires approaches to effectively detect and resolve disturbances to ensure optimal operations. While the control system can compensate for many types of disturban…
Bayesian method predicts runtime metrics for fog manufacturing.
problem Accurate prediction of runtime performance metrics in fog manufacturing.
method Bayesian sparse regression for multivariate mixed responses.
result Enhanced prediction and statistical inferences of runtime metrics.
In manufacture, steel and other metals are mainly cut and shaped during the fabrication process by computer numerical control (CNC) machines. To keep high productivity and efficiency of the fabrication process, engineers need to monitor the real-time process of CNC machines, and the lifetime management of machine tools…
A novel method classifies wafer defects using topological data analysis.
problem Classifying defect patterns on semiconductor wafers for maintenance and yield management.
method Representing defect patterns as vectors using topological features from persistent homology.
result The method outperforms CNN in accuracy and efficiency, especially with limited data.
Study examines cash conversion cycle in manufacturing firms, finding negative relationships with profitability and size.
problem Understanding cash conversion cycle in manufacturing firms and its impact on profitability and size.
method Empirical study of 30 manufacturing firms in Dhaka Stock Exchanges, categorizing them into six industries, analyzing industry averages and relationships with size and profitability.
result Negative relationship between cash conversion cycle and profitability, especially ROE; negative relationship with firm size in terms of net sales.
Paper proposes TBSD for efficient anomaly detection in textured images.
problem Challenges in anomaly detection for textured images, especially in manufacturing systems.
method Texture basis integrated smooth decomposition (TBSD) approach.
result TBSD surpasses benchmarks with less misidentification and superior performance.
Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machine-learning algorithms, but also energy-efficient computational models when implemented in VLSI circuits. In this paper, we propose a novel …
Efficient dispatching rule in manufacturing industry is key to ensure product on-time delivery and minimum past-due and inventory cost. Manufacturing, especially in the developed world, is moving towards on-demand manufacturing meaning a high mix, low volume product mix. This requires efficient dispatching that can wor…
Image post-processing is used in clinical-grade ultrasound scanners to improve image quality (e.g., reduce speckle noise and enhance contrast). These post-processing techniques vary across manufacturers and are generally kept proprietary, which presents a challenge for researchers looking to match current clinical-grad…
AI monitors social distancing and masks at manufacturing plants.
problem Ensuring safety of workers during post-COVID production.
method Computer vision and AI techniques for social distancing and mask detection.
result Real-time alerts prevent violations of social distancing and mask-wearing.
This communication is based on an original approach linking economical factors to technical and methodological ones. This work is applied to the decision process for mix production. This approach is relevant for costing driving systems. The main interesting point is that the quotation factors (linked to time indicators…
CICME estimates common and domain-specific causal mechanisms from multi-sensor data.
problem Inferring causal mechanisms from heterogeneous multi-sensor data across multiple domains.
method Three-step approach using Causal Transfer Learning (CTL).
result CICME reliably detects domain-invariant causal mechanisms and guides individual domain causal mechanism estimation.
New methods improve tool-to-tool matching in semiconductor manufacturing.
problem Challenges in obtaining static configuration data and extending methods to heterogeneous equipment.
method Novel TTTM analysis pipelines hypothesizing higher variance and modes for mismatched equipment.
result Best univariate method achieves correlation coefficients >0.95 and >0.5 with variance and modes, respectively.
The high-dimensionality and volume of large scale multistream data has inhibited significant research progress in developing an integrated monitoring and diagnostics (M&D) approach. This data, also categorized as big data, is becoming common in manufacturing plants. In this paper, we propose an integrated M\&D approach…
Labor productivity was studied at the microscopic level in terms of distributions based on individual firm financial data from Japan and the US. A power-law distribution in terms of firms and sector productivity was found in both countries' data. The labor productivities were not equal for nation and sectors, in contra…