T
Ting Chang
Researcher at University of Michigan
Publications - 15
Citations - 6582
Ting Chang is an academic researcher from University of Michigan. The author has contributed to research in topics: Memristor & Neuromorphic engineering. The author has an hindex of 11, co-authored 15 publications receiving 5570 citations.
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Nanoscale Memristor Device as Synapse in Neuromorphic Systems
TL;DR: A nanoscale silicon-based memristor device is experimentally demonstrated and it is shown that a hybrid system composed of complementary metal-oxide semiconductor neurons and Memristor synapses can support important synaptic functions such as spike timing dependent plasticity.
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Observation of conducting filament growth in nanoscale resistive memories
TL;DR: It is found that the filament growth can be dominated by cation transport in the dielectric film, and two different growth modes were observed for the first time in materials with different microstructures.
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Short-term memory to long-term memory transition in a nanoscale memristor.
Ting Chang,Sung Hyun Jo,Wei Lu +2 more
TL;DR: This study shows experimentally that the retention loss in a nanoscale memristor device bears striking resemblance to memory loss in biological systems and confirms that not only the shape or the total number of stimuli is influential, but also the time interval between stimulation pulses plays a crucial role in determining the effectiveness of the transition.
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Synaptic behaviors and modeling of a metal oxide memristive device
TL;DR: In this article, the memristive behavior is attributed to the migration of oxygen vacancies upon bias which modulates the interplay between Schottky barrier emission and tunneling at the WOX/electrode interface.
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Biorealistic Implementation of Synaptic Functions with Oxide Memristors through Internal Ionic Dynamics
TL;DR: It is shown that by taking advantage of the different time scales of internal oxygen vacancy (VO) dynamics in an oxide‐based memristor, diverse synaptic functions at different time scale can be implemented naturally.