Narrow Heater Bottom Electrode‐Based Phase Change Memory as a Bidirectional Artificial Synapse. (16th July 2018) - La Barbera, Selina; Ly, Denys R. B.; Navarro, Gabriele; Castellani, Niccolò; Cueto, Olga; Bourgeois, Guillaume; De Salvo, Barbara; Nowak, Etienne; Querlioz, Damien; Vianello, Elisa
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. Abstract : The narrow heater bottom electrode‐based phase change memory is introduced as an adapted engineered integration solution for artificial bidirectional synapse. This study demonstrates experimentally through an innovative programming strategy that a single device can implement gradual long‐term potentiation and depression. The natural nonlinearity of the conductance response to identical programming pulses improves the network performances for unsupervised learning using spike timing dependent plasticity.