Liquid-Based Memory Devices for Next-Generation Computing

[1]  J. D. del Alamo,et al.  Dynamics of PSG-Based Nanosecond Protonic Programmable Resistors for Analog Deep Learning , 2022, 2022 International Electron Devices Meeting (IEDM).

[2]  Su‐Ting Han,et al.  Synaptic plasticity in self-powered artificial striate cortex for binocular orientation selectivity , 2022, Nature Communications.

[3]  Jang-Sik Lee,et al.  Emulating the Signal Transmission in a Neural System Using Polymer Membranes. , 2022, ACS applied materials & interfaces.

[4]  H. Akinaga,et al.  Dynamic Nonlinear Behavior of Ionic Liquid-Based Reservoir Computing Devices , 2022, ACS applied materials & interfaces.

[5]  W. Hwang,et al.  Synaptic Current Response of a Liquid Ga Electrode via a Surface Electrochemical Redox Reaction in a NaOH Solution , 2022, ACS omega.

[6]  W. Lu,et al.  Memristive technologies for data storage, computation, encryption, and radio-frequency communication , 2022, Science.

[7]  Mahesh Y. Chougale,et al.  Bioinspired Soft Multistate Resistive Memory Device Based on Silk Fibroin Gel for Neuromorphic Computing , 2022, Advanced Engineering Materials.

[8]  Jang-Sik Lee,et al.  Neurotransmitter‐Induced Excitatory and Inhibitory Functions in Artificial Synapses , 2022, Advanced Functional Materials.

[9]  R. John.  An adaptive device for AI neural networks , 2022, Science.

[10]  Su‐Ting Han,et al.  2D Heterostructure for High‐Order Spatiotemporal Information Processing , 2021, Advanced Functional Materials.

[11]  Guozhao Fang,et al.  Ion migration and defect effect of electrode materials in multivalent-ion batteries , 2021, Progress in Materials Science.

[12]  T. Trung,et al.  Stretchable and Stable Electrolyte‐Gated Organic Electrochemical Transistor Synapse with a Nafion Membrane for Enhanced Synaptic Properties , 2021, Advanced Engineering Materials.

[13]  X. Miao,et al.  2D materials–based homogeneous transistor-memory architecture for neuromorphic hardware , 2021, Science.

[14]  Xu Hou,et al.  Bioinspired nanofluidic iontronics , 2021, Science.

[15]  K. Leo,et al.  Directed Growth of Dendritic Polymer Networks for Organic Electrochemical Transistors and Artificial Synapses , 2021, Advanced Electronic Materials.

[16]  W. G. van der Wiel,et al.  The rise of intelligent matter , 2021, Nature.

[17]  L. Bocquet,et al.  Modeling of emergent memory and voltage spiking in ionic transport through angstrom-scale slits , 2021, Science.

[18]  Adnan Mehonic,et al.  Brain-inspired computing needs a master plan , 2021, Nature.

[19]  Shuo Zhang,et al.  Multiplexed Neurotransmission Emulated by a p–n Cross Nanowire Synaptic Transistor for Satiety, Depression, and Drug Withdrawal , 2021, Advanced Functional Materials.

[20]  Guo Ping Wang,et al.  Electronic synapses mimicked in bilayer organic-inorganic heterojunction based memristor , 2021 .

[21]  Tae Geun Kim,et al.  Synaptic learning functionalities of inverse biomemristive device based on trypsin for artificial intelligence application , 2021 .

[22]  Min-Kyu Kim,et al.  Emerging Materials for Neuromorphic Devices and Systems , 2020, iScience.

[23]  Jang‐Sik Lee,et al.  Designing artificial sodium ion reservoirs to emulate biological synapses , 2020, NPG Asia Materials.

[24]  Youngjune Park,et al.  Artificial synaptic transistors based on Schottky barrier height modulation using reduced graphene oxides , 2020, Carbon.

[25]  Anuj Grover,et al.  Resistive Random Access Memory: A Review of Device Challenges , 2019, IETE Technical Review.

[26]  Butler W. Lampson,et al.  There’s plenty of room at the Top: What will drive computer performance after Moore’s law? , 2020, Science.

[27]  Youngjune Park,et al.  Heterosynaptic Plasticity Emulated by Liquid Crystal-Carbon Nanotube Composites with Modulatory Interneurons. , 2020, ACS applied materials & interfaces.

[28]  Farooq Ahmad Khanday,et al.  Resistive Random Access Memory (RRAM): an Overview of Materials, Switching Mechanism, Performance, Multilevel Cell (mlc) Storage, Modeling, and Applications , 2020, Nanoscale Research Letters.

[29]  M. Stiles,et al.  Neuromorphic spintronics , 2020, Nature Electronics.

[30]  Mark C. Hersam,et al.  Neuromorphic nanoelectronic materials , 2020, Nature Nanotechnology.

[31]  Lydéric Bocquet,et al.  Nanofluidics coming of age , 2020, Nature Materials.

[32]  Christine Grienberger,et al.  Synaptic Plasticity Forms and Functions. , 2020, Annual review of neuroscience.

[33]  M. Teodorescu,et al.  A Microfluidic Ion Sensor Array. , 2020, Small.

[34]  S. Ambrogio,et al.  Emerging neuromorphic devices , 2019, Nanotechnology.

[35]  Kaushik Roy,et al.  Towards spike-based machine intelligence with neuromorphic computing , 2019, Nature.

[36]  Mahesh Y. Chougale,et al.  Memristive switching in ionic liquid–based two-terminal discrete devices , 2019, Ionics.

[37]  X. Miao,et al.  Nanochannel-Based Transport in an Interfacial Memristor Can Emulate the Analog Weight Modulation of Synapses. , 2019, Nano letters.

[38]  Xiaobing Yan,et al.  Overview of Resistive Random Access Memory (RRAM): Materials, Filament Mechanisms, Performance Optimization, and Prospects , 2019, physica status solidi (RRL) – Rapid Research Letters.

[39]  Xiaojian Zhu,et al.  Nanoionic Resistive‐Switching Devices , 2019, Advanced Electronic Materials.

[40]  T. Dongale,et al.  MemSens: a new detection method for heavy metals based on silver nanoparticle assisted memristive switching principle , 2019, Journal of Materials Science: Materials in Electronics.

[41]  Jang‐Sik Lee,et al.  Ferroelectric Analog Synaptic Transistors. , 2019, Nano letters.

[42]  Jeonghyun Kim,et al.  Battery-free, skin-interfaced microfluidic/electronic systems for simultaneous electrochemical, colorimetric, and volumetric analysis of sweat , 2019, Science Advances.

[43]  O. Inganäs,et al.  Active Materials for Organic Electrochemical Transistors , 2018, Advanced materials.

[44]  Armantas Melianas,et al.  Organic electronics for neuromorphic computing , 2018, Nature Electronics.

[45]  H.-S. Philip Wong,et al.  In-memory computing with resistive switching devices , 2018, Nature Electronics.

[46]  Zhenan Bao,et al.  A bioinspired flexible organic artificial afferent nerve , 2018, Science.

[47]  W. Lew,et al.  Oxide-based RRAM materials for neuromorphic computing , 2018, Journal of Materials Science.

[48]  Jang-Sik Lee,et al.  Short-Term Plasticity and Long-Term Potentiation in Artificial Biosynapses with Diffusive Dynamics. , 2018, ACS nano.

[49]  Youngjune Park,et al.  Artificial Synapses with Short- and Long-Term Memory for Spiking Neural Networks Based on Renewable Materials. , 2017, ACS nano.

[50]  D. Andelman,et al.  Bjerrum pairs in ionic solutions: A Poisson-Boltzmann approach. , 2017, The Journal of chemical physics.

[51]  Guangyu R. Yang,et al.  Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework , 2016, PLoS Comput. Biol..

[52]  Rajanish K. Kamat,et al.  Development of Ag/ZnO/FTO thin film memristor using aqueous chemical route , 2015 .

[53]  Qiangfei Xia,et al.  Nanoscale memristive radiofrequency switches , 2015, Nature Communications.

[54]  H-S Philip Wong,et al.  Memory leads the way to better computing. , 2015, Nature nanotechnology.

[55]  Igor L. Markov,et al.  Limits on fundamental limits to computation , 2014, Nature.

[56]  T. Serrano-Gotarredona,et al.  STDP and STDP variations with memristors for spiking neuromorphic learning systems , 2013, Front. Neurosci..

[57]  Qi Liu,et al.  Real‐Time Observation on Dynamic Growth/Dissolution of Conductive Filaments in Oxide‐Electrolyte‐Based ReRAM , 2012, Advanced materials.

[58]  Sung Jae Kim,et al.  Nanofluidic concentration devices for biomolecules utilizing ion concentration polarization: theory, fabrication, and applications. , 2010, Chemical Society reviews.

[59]  Lucy J. Colwell,et al.  Action Potential Initiation in the Hodgkin-Huxley Model , 2009, PLoS Comput. Biol..

[60]  Wulfram Gerstner,et al.  Phenomenological models of synaptic plasticity based on spike timing , 2008, Biological Cybernetics.

[61]  R. Waser,et al.  Nanoionics-based resistive switching memories. , 2007, Nature materials.

[62]  Nektarios Tavernarakis,et al.  The role of synaptic ion channels in synaptic plasticity , 2006, EMBO reports.

[63]  A. Travesset,et al.  Bjerrum Pairing Correlations at Charged Interfaces , 2006, cond-mat/0603834.

[64]  J. Eijkel,et al.  Nanofluidics: what is it and what can we expect from it? , 2005 .

[65]  A. Majumdar,et al.  Electrostatic control of ions and molecules in nanofluidic transistors. , 2005, Nano letters.

[66]  W. Abraham,et al.  Memory retention – the synaptic stability versus plasticity dilemma , 2005, Trends in Neurosciences.

[67]  J. Campbell Scott,et al.  Is There an Immortal Memory? , 2004, Science.

[68]  S. Quake,et al.  Microfluidic Large-Scale Integration , 2002, Science.

[69]  J. Meindl,et al.  Limits on silicon nanoelectronics for terascale integration. , 2001, Science.

[70]  M. Häusser.  The Hodgkin-Huxley theory of the action potential , 2000, Nature Neuroscience.

[71]  H. Sompolinsky,et al.  Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity , 1996, Science.

[72]  P. Wynblatt.  Calculation of the vacancy migration energy in cubic crystals , 1967 .

[73]  T. Dongale,et al.  Frugal discrete memristive device based on potassium permanganate solution , 2021, Materials Research Express.

[74]  J Joshua Yang,et al.  Memristive devices for computing. , 2013, Nature nanotechnology.

[75]  Zhigang Wu,et al.  Microfluidic electronics. , 2012, Lab on a chip.

[76]  W. Gerstner,et al.  Spike-Timing-Dependent Plasticity: A Comprehensive Overview , 2012, Front. Syn. Neurosci..

[77]  Matthieu Gilson,et al.  Frontiers in Computational Neuroscience Computational Neuroscience , 2022 .

[78]  L. Abbott,et al.  Synaptic computation , 2004, Nature.

[79]  S. J. Martin,et al.  Synaptic plasticity and memory: an evaluation of the hypothesis. , 2000, Annual review of neuroscience.