Research on Semiconductor Chip Grade Classification and Real-time Evaluation Method Based on Hybrid Artificial Intelligence Technology

Cong Xu [Global ETS]

Tuesday, June 27th - 4:15 pm

Abstract:

This study presents a semiconductor chip grade classification and real-time evaluation methodology based on hybrid artificial intelligence techniques, which significantly improves the accuracy and efficiency of the classification process. The proposed method integrates deep learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), with advanced optimization algorithms, including Genetic Algorithms and Particle Swarm Optimization. The deep learning models extract key features from semiconductor chip performance data, while the optimization algorithms enhance the classification and assessment process. In a case study, our methodology was applied to a large-scale manufacturer of semiconductor ADC (Analog-to-Digital Converter) chips, successfully identifying quality issues that could potentially lead to device failure. The hybrid artificial intelligence approach provides a flexible and adaptive framework, enabling the methodology to handle various types of semiconductor chips and adapt to different manufacturing processes. Through extensive experiments on real-world datasets, this method demonstrated superior performance in terms of classification accuracy, real-time evaluation, and generalization capabilities compared to traditional methods. Moreover, the method enables rapid identification of potential issues, facilitating proactive maintenance and reducing the risk of chip failure. This research contributes to the improvement of quality control in semiconductor chips and advances the field of counterfeit chip detection areas.

Biography:

Cong Xu [Global ETS]

Xu Cong is currently a Ph.D. candidate at the University of South Florida, specializing in the field of digital signal processing in electrical engineering. His research mainly focuses on the application and challenges of hybrid artificial intelligence in chip testing, the current state of research on acoustic digital twin systems and their prospects in the field of autonomous driving, the 3D localization system of multiple sound sources based on TDOA and tetrahedral microphone array, and the research on the real-time evaluation method of semiconductor chip grade classification based on hybrid artificial intelligence technology. His research achievements have been applied in the fully automatic chip functional testing and universal tester research at Global ETS's automated detection production line, creating a lot of potential value.

Prior to this, he served as the founder of the International Hybrid Artificial Intelligence Association, a non-profit organization. He currently serves as a technical advisor for the GETS and USF research cooperation project. His professional skills include robot technology, artificial intelligence algorithms, and digital signal processing. He has won the first prize (third place) in the ROBOCUP China competition, a global robot competition.

Xu Cong has made significant contributions in the semiconductor industry, especially in the field of chip counterfeiting detection. His work and research in this field have promoted the development of the chip industry. This also includes his work at Global ETS, where his technology has been applied to the company's automated detection production line, bringing significant potential value.

Dr. Diganta Das

For more information or questions regarding the technical program (including Professional Development Courses), contact the Conference Chair, Dr. Diganta Das.

Karlie Severinson

For more information or questions regarding event logistics, exhibitions, and sponsorship, contact Karlie Severinson.