Williams, R. Stanley

Memristive switching mechanism for metal/oxide/metal nanodevices

Yang, J. J., Pickett, M. D., Li, X., Ohlberg, D. A. A., Stewart, D. R., & Williams, R. S. (2008) · Nature Nanotechnology

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 2,987

Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing

Wang, Z., Joshi, S., Savel’ev, S. E., Jiang, H., Midya, R., Lin, P., Hu, M., Ge, N., Strachan, J. P., Li, Z., et al. (2017) · Nature Materials

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 2,358

‘Memristive’ switches enable ‘stateful’ logic operations via material implication

Borghetti, J., Snider, G. S., Kuekes, P. J., Yang, J. J., Stewart, D. R., & Williams, R. S. (2010) · Nature

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 1,929

Fully memristive neural networks for pattern classification with unsupervised learning

Wang, Z., Joshi, S., Savel’ev, S., Song, W., Midya, R., Li, Y., Rao, M., Yan, P., Asapu, S., Zhuo, Y., et al. (2018) · Nature Electronics

Advanced Memory and Neural ComputingNeural Networks and Reservoir Computing

Cited by 1,106

Efficient and self-adaptive in-situ learning in multilayer memristor neural networks

Li, C., Belkin, D., Li, Y., Yan, P., Hu, M., Ge, N., Jiang, H., Montgomery, E., Lin, P., Wang, Z., et al. (2018) · Nature Communications

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 931

ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars

Shafiee, A., Nag, A., Muralimanohar, N., Balasubramonian, R., Strachan, J. P., Hu, M., Williams, R. S., & Srikumar, V. (2016) · 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA)

Advanced Memory and Neural ComputingAdvanced Neural Network Applications

Cited by 775

Memristor‐Based Analog Computation and Neural Network Classification with a Dot Product Engine

Hu, M., Graves, C. E., Li, C., Li, Y., Ge, N., Montgomery, E., Davila, N., Jiang, H., Williams, R. S., Yang, J. J., et al. (2018) · Advanced Materials

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 751

Sub-nanosecond switching of a tantalum oxide memristor

Torrezan, A. C., Strachan, J. P., Medeiros-Ribeiro, G., & Williams, R. S. (2011) · Nanotechnology

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 701

How We Found The Missing Memristor

Williams, R. (2008) · IEEE Spectrum

Advanced Memory and Neural ComputingAdvancements in Semiconductor Devices and Circuit Design

Cited by 697

Memristor−CMOS Hybrid Integrated Circuits for Reconfigurable Logic

Xia, Q., Robinett, W., Cumbie, M. W., Banerjee, N., Cardinali, T. J., Yang, J. J., Wu, W., Li, X., Tong, W. M., Strukov, D. B., et al. (2009) · Nano Letters

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 671

High switching endurance in TaOx memristive devices

Yang, J. J., Zhang, M.-X., Strachan, J. P., Miao, F., Pickett, M. D., Kelley, R. D., Medeiros-Ribeiro, G., & Williams, R. S. (2010) · Applied Physics Letters

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 637

PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference

Ankit, A., Hajj, I. E., Chalamalasetti, S. R., Ndu, G., Foltin, M., Williams, R. S., Faraboschi, P., Hwu, W.-M. W., Strachan, J. P., Roy, K., et al. (2019) · Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems

Advanced Memory and Neural ComputingParallel Computing and Optimization Techniques

Cited by 424

High‐Speed and Low‐Energy Nitride Memristors

Choi, B. J., Torrezan, A. C., Strachan, J. P., Kotula, P. G., Lohn, A. J., Marinella, M. J., Li, Z., Williams, R. S., & Yang, J. J. (2016) · Advanced Functional Materials

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 356

In situ training of feed-forward and recurrent convolutional memristor networks

Wang, Z., Li, C., Lin, P., Rao, M., Nie, Y., Song, W., Qiu, Q., Li, Y., Yan, P., Strachan, J. P., et al. (2019) · Nature Machine Intelligence

Advanced Memory and Neural ComputingNeural Networks and Applications

Cited by 307

A hybrid nanomemristor/transistor logic circuit capable of self-programming

Borghetti, J., Li, Z., Straznicky, J., Li, X., Ohlberg, D. A. A., Wu, W., Stewart, D. R., & Williams, R. S. (2009) · Proceedings of the National Academy of Sciences

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 270

Low-Power, Self-Rectifying, and Forming-Free Memristor with an Asymmetric Programing Voltage for a High-Density Crossbar Application

Kim, K. M., Zhang, J., Graves, C., Yang, J. J., Choi, B. J., Hwang, C. S., Li, Z., & Williams, R. S. (2016) · Nano Letters

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 246

Four-dimensional address topology for circuits with stacked multilayer crossbar arrays

Strukov, D. B., & Williams, R. S. (2009) · Proceedings of the National Academy of Sciences

Advanced Memory and Neural Computing3D IC and TSV technologies

Cited by 153

Quantized conductance coincides with state instability and excess noise in tantalum oxide memristors

Yi, W., Savel'ev, S. E., Medeiros-Ribeiro, G., Miao, F., Zhang, M.-X., Yang, J. J., Bratkovsky, A. M., & Williams, R. S. (2016) · Nature Communications

Advanced Memory and Neural ComputingMolecular Junctions and Nanostructures

Cited by 141

Selective Surface Functionalization of Silicon Nanowires via Nanoscale Joule Heating

Park, I., Li, Z., Pisano, A. P., & Williams, R. S. (2007) · Nano Letters

Nanowire Synthesis and ApplicationsSilicon Nanostructures and Photoluminescence

Cited by 123

Measuring the switching dynamics and energy efficiency of tantalum oxide memristors

Strachan, J. P., Torrezan, A. C., Medeiros-Ribeiro, G., & Williams, R. S. (2011) · Nanotechnology

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 114

Spectromicroscopy of tantalum oxide memristors

Strachan, J. P., Medeiros-Ribeiro, G., Yang, J. J., Zhang, M.-X., Miao, F., Goldfarb, I., Holt, M., Rose, V., & Williams, R. S. (2011) · Applied Physics Letters

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 90

Activity-difference training of deep neural networks using memristor crossbars

Yi, S.-I., Kendall, J. D., Williams, R. S., & Kumar, S. (2022) · Nature Electronics

Advanced Memory and Neural ComputingNeural Networks and Applications

Cited by 75

Integration of 4F2 selector-less crossbar array 2Mb ReRAM based on transition metal oxides for high density memory applications

Lee, H. D., Kim, S. G., Cho, K., Hwang, H., Choi, H., Lee, J., Lee, S. H., Lee, H. J., Suh, J., Chung, S.-O., et al. (2012) · 2012 Symposium on VLSI Technology (VLSIT)

Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices

Cited by 68

Resistor-logic demultiplexers for nanoelectronics based on constant-weight codes

Kuekes, P. J., Robinett, W., Roth, R. M., Seroussi, G., Snider, G. S., & Williams, R. S. (2006) · Nanotechnology

Advanced Memory and Neural ComputingCoding theory and cryptography

Cited by 40