Using Artificial Neural Back-Propagation Network Model to Detect the Outliers in Semiconductor Manufacturing Machines

Yang, Kai-Chieh; Huang, Chia-Hui; Yang, Chih‐Hsiang; Chao, Pei-Yao; Shih, Po-Hong

doi:10.1007/978-3-319-07467-2_26

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Figure 10 shows the subsequent results of approximated curves of the sensors with different numbers of layers. BP is a popular neural network training algorithm used to adjust the network weights and minimize the difference between the predicted and actual values [45]. In this case, the target input was the estimated resistance value obtained from the polynomial regression, and the neural network was trained to predict this value based on the input force values.…”

Section: Modified Hysteresis: Simulation and Experimentsmentioning

confidence: 99%

Modified Nonlinear Hysteresis Approach for a Tactile Sensor

Abdul-Hussain,

Holderbaum,

Theodoridis

et al. 2023

Sensors

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Soft tactile sensors based on piezoresistive materials have large-area sensing applications. However, their accuracy is often affected by hysteresis which poses a significant challenge during operation. This paper introduces a novel approach that employs a backpropagation (BP) neural network to address the hysteresis nonlinearity in conductive fiber-based tactile sensors. To assess the effectiveness of the proposed method, four sensor units were designed. These sensor units underwent force sequences to collect corresponding output resistance. A backpropagation network was trained using these sequences, thereby correcting the resistance values. The training process exhibited excellent convergence, effectively adjusting the network’s parameters to minimize the error between predicted and actual resistance values. As a result, the trained BP network accurately predicted the output resistances. Several validation experiments were conducted to highlight the primary contribution of this research. The proposed method reduced the maximum hysteresis error from 24.2% of the sensor’s full-scale output to 13.5%. This improvement established the approach as a promising solution for enhancing the accuracy of soft tactile sensors based on piezoresistive materials. By effectively mitigating hysteresis nonlinearity, the capabilities of soft tactile sensors in various applications can be enhanced. These sensors become more reliable and more efficient tools for the measurement and control of force, particularly in the fields of soft robotics and wearable technology. Consequently, their widespread applications extend to robotics, medical devices, consumer electronics, and gaming. Though the complete elimination of hysteresis in tactile sensors may not be feasible, the proposed method effectively modifies the hysteresis nonlinearity, leading to improved sensor output accuracy.

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Section: Modified Hysteresis: Simulation and Experimentsmentioning

confidence: 99%

Modified Nonlinear Hysteresis Approach for a Tactile Sensor

Abdul-Hussain,

Holderbaum,

Theodoridis

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Modified Nonlinear Hysteresis Approach of a Tactile Sensor

Hussain¹,

Holderbaum²,

Theodoridis³

et al. 2023

Preprint

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Soft tactile sensors based on piezoresistive materials have various large-area sensing applications. However, their accuracy is often affected by hysteresis, a phenomenon that poses a significant challenge during operation. This paper introduces a novel approach that employs a backpropagation (BP) neural network to address the hysteresis nonlinearity in conductive fibre-based tactile sensors. To assess the effectiveness of the proposed method, four sensor units with different layer configurations (1, 3, 6, and 12) were designed. These sensor units underwent force sequences to collect corresponding output resistances. A backpropagation network was trained using these force sequences, thereby correcting the resistance values. The training process exhibited excellent convergence, effectively adjusting the network's parameters to minimize the error between predicted and actual resistance values. As a result, the trained BP network accurately predicted the output resistances. Several validation experiments were conducted to highlight the primary contribution of this research. The proposed method reduced the maximum hysteresis error from 24.2% of the sensor's full-scale output to 13.5%. This improvement establishes the approach as a promising solution for enhancing the accuracy of soft tactile sensors based on piezoresistive materials. By effectively mitigating hysteresis nonlinearity, the capabilities of soft tactile sensors in various applications can be enhanced. These sensors become more reliable and more efficient tools for force measurement and control, particularly in the fields of soft robotics and wearable technology. Consequently, their widespread applications extend to robotics, medical devices, consumer electronics, and gaming. Notably, the complete elimination of hysteresis in tactile sensors may not be feasible. Nevertheless, the proposed method effectively modifies the hysteresis nonlinearity, leading to improved sensor output accuracy.

show abstract

Using Artificial Neural Back-Propagation Network Model to Detect the Outliers in Semiconductor Manufacturing Machines

Cited by 2 publications

References 13 publications

Modified Nonlinear Hysteresis Approach for a Tactile Sensor

Modified Nonlinear Hysteresis Approach for a Tactile Sensor

Modified Nonlinear Hysteresis Approach of a Tactile Sensor

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