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Ambipolar carbon nanotube based field-effect transistors (AP-CNFETs) exhibitunique electrical characteristics, such as tri-state operation andbi-directionality, enabling systems with complex and reconfigurable computing.In this paper, AP-CNFETs are used to design a mixed-signal machine learning(ML) classifier. The classifier is designed in SPICE with feature size of 15 nmand operates at 250 MHz. The system is demonstrated based on MNIST digitdataset, yielding 90% accuracy and no accuracy degradation as compared with theclassification of this dataset in Python. The system also exhibits lower powerconsumption and smaller physical size as compared with the state-of-the-artCMOS and memristor based mixed-signal classifiers.

Exploiting Dual-Gate Ambipolar CNFETs for Scalable Machine Learning Classification