Combined Machine Learning Techniques for Characteristics Classification and Threshold Voltage Extraction of Transistors

Kocak H. M., Mitard J., NASKALİ A. T.

34th IEEE International Conference on Microelectronic Test Structures, ICMTS 2022, Ohio, Amerika Birleşik Devletleri, 21 Mart - 15 Nisan 2022, cilt.2022-March identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 2022-March
  • Doi Numarası: 10.1109/icmts50340.2022.9898251
  • Basıldığı Şehir: Ohio
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Anahtar Kelimeler: Semiconductors, Transistor defect, Machine Learning, Convolutional Neural Network, Threshold Voltage Extraction, DEFECT PATTERNS
  • Galatasaray Üniversitesi Adresli: Evet

Özet

© 2022 IEEE.We present an autonomous 2-step Machine Learning based approach for characteristics classification and key parameter extraction for transistors. The first step is a multi-model ensemble, composed of Machine Learning, more particularly a Convolutional Neural Network (CNN) approach to enable fast classification of transistor characteristics. The second step is another CNN model to extract the threshold voltage parameters that enable us to measure the performance of ultra-scaled MOSFETs. Our CNN-based classifier has demonstrated accuracy above 90% with an execution time significantly faster than that of the current human expert-based methods while our Vth extractor has demonstrated less than 8mV error rate in the filtered dataset that comes from the classifier. The proposed techniques which do not incorporate hard-coded domain knowledge, and are also tested with input data coming from 16nm-node FinFET technology and evaluated by a consensus of experts to prove the universality of the models using the same parameters.