Choi, Shinhyun
Korea Advanced Institute of Science and Technology
9 papers on CoChatORCID 0000-0002-5852-3360
Most cited papers
- Verified
SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations
Choi, S., Tan, S. H., Li, Z., Kim, Y., Choi, C., Chen, P.-Y., Yeon, H., Yu, S., & Kim, J. (2018) · Nature Materials
Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices
Cited by 690
- Verified
Experimental Demonstration of a Second-Order Memristor and Its Ability to Biorealistically Implement Synaptic Plasticity
Kim, S., Du, C., Sheridan, P., Ma, W., Choi, S., & Lu, W. D. (2015) · Nano Letters
Advanced Memory and Neural ComputingNeural dynamics and brain function
Cited by 603
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Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing
Park, S.-O., Jeong, H., Park, J., Bae, J., & Choi, S. (2022) · Nature Communications
Advanced Memory and Neural ComputingNeural Networks and Applications
Cited by 331
- Verified
Stochastic memristive devices for computing and neuromorphic applications
Gaba, S., Sheridan, P., Zhou, J., Choi, S., & Lu, W. (2013) · Nanoscale
Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices
Cited by 307
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Data Clustering using Memristor Networks
Choi, S., Sheridan, P., & Lu, W. D. (2015) · Scientific Reports
Advanced Memory and Neural ComputingNeural Networks and Applications
Cited by 132
- Verified
Tuning Resistive Switching Characteristics of Tantalum Oxide Memristors through Si Doping
Kim, S., Choi, S., Lee, J., & Lu, W. D. (2014) · ACS Nano
Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices
Cited by 129
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Retention Secured Nonlinear and Self-Rectifying Analog Charge Trap Memristor for Energy-Efficient Neuromorphic Hardware
Kim, G., Son, S., Song, H., Jeon, J. B., Lee, J., Cheong, W. H., Choi, S., & Kim, K. M. (2023) · Advanced Science
Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices
Cited by 72
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Linear conductance update improvement of CMOS-compatible second-order memristors for fast and energy-efficient training of a neural network using a memristor crossbar array
Park, S.-O., Park, T., Jeong, H., Hong, S., Seo, S., Kwon, Y., Lee, J., & Choi, S. (2023) · Nanoscale Horizons
Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices
Cited by 31
- Verified
Improvement of RRAM Device Performance Through On-Chip Resistors
Gaba, S., Choi, S., Sheridan, P., Chang, T., Yang, Y., & Lu, W. (2012) · MRS Proceedings
Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices
Cited by 2
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