More Like this

1. Operation and Productivity Monitoring from Sound Signal of Legacy Pipe Bending Machine via Convolutional Neural Network (CNN)

   https://doi.org/10.1007/s12541-024-01018-3  

   Kim, Eunseob; Mun, Daeseong; Jun, Martin_B_G; Yun, Huitaek (April 2024, International Journal of Precision Engineering and Manufacturing)

   Abstract This study introduces a non-invasive approach to monitor operation and productivity of a legacy pipe bending machine in real-time based on a lightweight convolutional neural network (CNN) model and internal sound as input data. Various sensors were deployed to determine the optimal sensor type and placement, and labels for training and testing the CNN model were generated through the meticulous collection of sound data in conjunction with webcam videos. The CNN model, which was optimized through hyperparameter tuning via grid search and utilized feature extraction using Log-Mel spectrogram, demonstrated notable prediction accuracies in the test. However, when applied in a real-world manufacturing scenario, the model encountered a significant number of errors in predicting productivity. To navigate through this challenge and enhance the predictive accuracy of the system, a buffer algorithm using the inferences of CNN models was proposed. This algorithm employs a queuing method for continuous sound monitoring securing robust predictions, refines the interpretation of the CNN model inferences, and enhances prediction outcomes in actual implementation where accuracy of monitoring productivity information is crucial. The proposed lightweight CNN model alongside the buffer algorithm was successfully deployed on an edge computer, enabling real-time remote monitoring. 

   more » « less
2. A novel method for through-silicon via characterization based on diffraction fringe analysis

   https://doi.org/10.1016/j.ultramic.2025.114136  

   Lin, Pengfei; Lu, Kuan; Lee, ChaBum (June 2025, Ultramicroscopy)

   The precision metrology of through-hole silicon via (TSV) in the semiconductor industry has remained a critical challenge as its critical dimension (CD) reduces. In this letter, we report a novel method for TSV geometric feature measurement and characterization. By illuminating a collimated infrared laser beam to the TSV and then analyzing the TSV edge-induced diffraction interferometric fringe patterns, multiple geometric information of the TSV could be characterized, establishing its database. This computational approach to TSV characterization was validated by experiments. Being non-destructive and easy to deploy, this method provides a low cost and high efficiency solution for TSV metrology. 

   more » « less
3. Efficient Optimized Testing of Resistive RAM Based Convolutional Neural Networks

   https://doi.org/10.1109/IOLTS60994.2024.10616092  

   Saha, Anurup; Ma, Kwondo; Amarnath, Chandramouli; Chatterjee, Abhijit (July 2024, IEEE)

   Resistive random access memory (RRAM) based memristive crossbar arrays enable low power and low latency inference for convolutional neural networks (CNNs), making them suitable for deployment in IoT and edge devices. However, RRAM cells within a crossbar suffer from conductance variations, making RRAM-based CNNs vulnerable to degradation of their classification accuracy. To address this, the classification accuracy of RRAM based CNN chips can be estimated using predictive tests, where a trained regressor predicts the accuracy of a CNN chip from the CNN’s response to a compact test dataset. In this research, we present a framework for co-optimizing the pixels of the compact test dataset and the regressor. The novelty of the proposed approach lies in the ability to co-optimize individual image pixels, overcoming barriers posed by the computational complexity of optimizing the large numbers of pixels in an image using state-of-the-art techniques. The co-optimization problem is solved using a three step process: a greedy image downselection followed by backpropagation driven image optimization and regressor fine-tuning. Experiments show that the proposed test approach reduces the CNN classification accuracy prediction error by 31% compared to the state of the art. It is seen that a compact test dataset with only 2-4 images is needed for testing, making the scheme suitable for built-in test applications. 

   more » « less
4. Efficient Optimized Testing of Resistive RAM Based Convolutional Neural Networks

   Saha, A; Ma, K; Amarnath, C; Chatterjee, A (August 2024, IEEE)

   Resistive random access memory (RRAM) based memristive crossbar arrays enable low power and low latency inference for convolutional neural networks (CNNs), making them suitable for deployment in IoT and edge devices. However, RRAM cells within a crossbar suffer from conductance variations, making RRAM-based CNNs vulnerable to degradation of their classification accuracy. To address this, the classification accuracy of RRAM based CNN chips can be estimated using predictive tests, where a trained regressor predicts the accuracy of a CNN chip from the CNN’s response to a compact test dataset. In this research, we present a framework for co-optimizing the pixels of the compact test dataset and the regressor. The novelty of the proposed approach lies in the ability to co-optimize individual image pixels, overcoming barriers posed by the computational complexity of optimizing the large numbers of pixels in an image using state-of-the-art techniques. The co-optimization problem is solved using a three step process: a greedy image downselection followed by backpropagation driven image optimization and regressor fine-tuning. Experiments show that the proposed test approach reduces the CNN classification accuracy prediction error by 31% compared to the state of the art. It is seen that a compact test dataset with only 2-4 images is needed for testing, making the scheme suitable for built-in test applications. 

   more » « less
5. Structural health monitoring using extremely compressed data through deep learning

   https://doi.org/10.1111/mice.12517  

   Azimi, Mohsen; Pekcan, Gokhan (November 2019, Computer-Aided Civil and Infrastructure Engineering)

   Abstract This study introduces a novel convolutional neural network (CNN)‐based approach for structural health monitoring (SHM) that exploits a form of measured compressed response data through transfer learning (TL)‐based techniques. The implementation of the proposed methodology allows damage identification and localization within a realistic large‐scale system. To validate the proposed method, first, a well‐known benchmark model is numerically simulated. Using acceleration response histories, as well as compressed response data in terms of discrete histograms, CNN models are trained, and the robustness of the CNN architectures is evaluated. Finally, pretrained CNNs are fine‐tuned to be adaptable for three‐parameter, extremely compressed response data, based on the response mean, standard deviation, and a scale factor. The performance of each CNN implementation is assessed using training accuracy histories as well as confusion matrices, along with other performance metrics. In addition to the numerical study, the performance of the proposed method is demonstrated using experimental vibration response data for verification and validation. The results indicate that deep TL can be implemented effectively for SHM of similar structural systems with different types of sensors. 

   more » « less