Zhang, Qingtian

Fully hardware-implemented memristor convolutional neural network

Yao, P., Wu, H., Gao, B., Tang, J., Zhang, Q., Zhang, W., Yang, J. J., & Qian, H. (2020) · Nature

Advanced Memory and Neural ComputingNeural Networks and Reservoir Computing

Cited by 2,179

Face classification using electronic synapses

Yao, P., Wu, H., Gao, B., Eryilmaz, S. B., Huang, X., Zhang, W., Zhang, Q., Deng, N., Shi, L., Wong, H.-S. P., et al. (2017) · Nature Communications

Advanced Memory and Neural ComputingNeural Networks and Reservoir Computing

Cited by 909

An in-memory computing architecture based on a duplex two-dimensional material structure for in situ machine learning

Ning, H., Yu, Z., Zhang, Q., Wen, H., Gao, B., Mao, Y., Li, Y., Zhou, Y., Zhou, Y., Chen, J., et al. (2023) · Nature Nanotechnology

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 198

A Reliable All‐2D Materials Artificial Synapse for High Energy‐Efficient Neuromorphic Computing

Tang, J., He, C., Tang, J., Yue, K., Zhang, Q., Liu, Y., Wang, Q., Wang, S., Li, N., Shen, C., et al. (2021) · Advanced Functional Materials

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 163

Investigation of statistical retention of filamentary analog RRAM for neuromophic computing

Zhao, M., Wu, H., Gao, B., Zhang, Q., Wu, W., Wang, S., Xi, Y., Wu, D., Deng, N., Yu, S., et al. (2017) · 2017 IEEE International Electron Devices Meeting (IEDM)

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 95