Abstract
Parallel information processing, energy efficiency and unsupervised learning make the human brain a model computing system for unstructured data handling. Oxide memristors, from filamentary to metal-insulator transition devices have been shown to emulate synaptic and neuronal functionalities in artificial neuromorphic circuits. However, challenges like non-linear conductance update, cycle-to-cycle and device-to-device variability and leakage current related issues in a dense crossbar structure are still not resolved. For neurons, often a circuit with multiple active or passive components are required which can complicate circuit designs in a large-scale neural network. In our earlier work, we reported solution-processable ferroelectric tunnel junctions (FTJs) with P(VDF-TrFE) copolymer barriers on semiconducting bottom electrode can show analog memristive behavior with a broad range of accessible conductance states and low energy dissipation of 100 fJ for the onset of depression and 1 pJ for the onset of potentiation by resetting small tunneling currents on nanosecond timescales. Key synaptic functions like programmable synaptic weight, long and short-term potentiation and depression, paired-pulse facilitation and depression, and Hebbian and anti-Hebbian learning through spike shape and timing-dependent plasticity are demonstrated. 1 In the current work, we demonstrate that by manipulating the carrier concentration of the semiconducting bottom electrode in FTJs, it is possible to control the dynamics of ferroelectric domain rotations in a way so that from synaptic devices they can act similar to neuronal devices. 2 These results offer a promising outlook for the FTJ memristors on semiconducting bottom electrodes as both synapse and neuron devices in artificial neural networks by controlling carrier doping concentrations only.
Original language | English |
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Publication status | Published - 2020 |
MoE publication type | Not Eligible |
Event | 2020 MRS Virtual Spring/Fall Meeting & Exhibit: Online - Virtual, Boston, United States Duration: 27 Nov 2020 → 4 Dec 2020 https://www.mrs.org/meetings-events/fall-meetings-exhibits/2020-mrs-spring-and-fall-meeting/ |
Conference
Conference | 2020 MRS Virtual Spring/Fall Meeting & Exhibit |
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Country/Territory | United States |
City | Boston |
Period | 27/11/20 → 4/12/20 |
Internet address |
Keywords
- Neuromorphic computing
- Ferroelectric tunnel junctions
- Neurons
- Memories