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Narrow Heater Bottom Electrode‐Based Phase Change Memory as a Bidirectional Artificial Synapse
Author(s) -
La Barbera Selina,
Ly Denys R. B.,
Navarro Gabriele,
Castellani Niccolò,
Cueto Olga,
Bourgeois Guillaume,
De Salvo Barbara,
Nowak Etienne,
Querlioz Damien,
Vianello Elisa
Publication year - 2018
Publication title -
advanced electronic materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.25
H-Index - 56
ISSN - 2199-160X
DOI - 10.1002/aelm.201800223
Subject(s) - neuromorphic engineering , materials science , long term potentiation , phase change memory , computer science , synapse , synaptic weight , initialization , process (computing) , artificial neural network , nanotechnology , neuroscience , artificial intelligence , biochemistry , chemistry , receptor , layer (electronics) , biology , programming language , operating system
Abstract Phase change memory can provide a remarkable artificial synapse for neuromorphic systems, as it features excellent reliability and can be used as an analog memory. However, this approach is complicated by the fact that crystallization and amorphization differ radically: crystallization can be realized in a very gradual manner, very similarly to synaptic potentiation, while the amorphization process tends to be abrupt, unlike synaptic depression. Addressing this non‐biorealism of amorphization requires system‐level solutions that have considerable energy cost or limit the generality of the approach. This work demonstrates experimentally that an adaptation of the memory structure associated with an initialization electrical pulse followed by a sequence of identical fast pulses can overcome this challenge. A single device can then naturally implement gradual long‐term potentiation and depression, much like synapses in biology. This study evidences through statistical measurements the reproducibility of the approach, discusses its physical origin, as well as the importance of the device architecture and of the initial electrical pulse. Through the use of system‐level simulation, it is shown that this device is especially adapted to a neuroscience‐inspired learning. These results highlight how nanodevices can be suitable for bioinspired applications while retaining the qualities of industrial technology.