Object detection in microelectronic circuits is critical for ensuring
design integrity, manufacturing precision, and fault tolerance. With
increasing circuit complexity and miniaturization, conventional
imaging and detection approaches often fail to deliver the required
accuracy and reliability. Existing object detection techniques struggle
with high-resolution micro-scale structures, suffer from high false
positives, and are computationally intensive. Moreover, integrating
detection techniques within MEMS-based systems remains a challenge
due to sensor limitations and noise sensitivity. This work proposes a
SEMI MEMS (Smart Electro-Mechanical Integrated Micro System)-
based supervised learning approach combining MEMS sensor data
with convolutional neural networks (CNNs) for real-time object
detection in microelectronic layouts. A custom-trained CNN is
integrated with signal data from capacitive MEMS sensors to enhance
feature extraction in noisy environments. The proposed method
achieves 96.2% detection accuracy, a 15.3% improvement over baseline
MEMS-CNN hybrids. Precision and recall values are 0.94 and 0.97,
respectively. Compared to existing methods, processing time decreased
by 22%, and false detection rate dropped by 18%.
Arvind Kumar Shukla1, K. Regin Bose2
IFTM University, India1, Rajalakshmi Institute of Technology, India2
MEMS Sensors, Supervised Learning, Object Detection, Microelectronics, CNN
| January | February | March | April | May | June | July | August | September | October | November | December |
| 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Published By :
ICTACT
Published In :
ICTACT Journal on Microelectronics
( Volume: 11 , Issue: 1 , Pages: 2027 - 2033 )
Date of Publication :
April 2025
Page Views :
32
Full Text Views :
7
|