Abstract
We present optically and electrically tunable conductance modifications of a site-controlled quantum-dot memristor. The conductance of the device is tuned by electron localization on a quantum dot. The control of the conductance with voltage and low-power light pulses enables applications in neuromorphic and arithmetic computing. As in neural networks, applying pre- and postsynaptic voltage pulses to the memristor allows us to increase (potentiation) or decrease (depression) the conductance by tuning the time difference between the electrical pulses. Exploiting state-dependent thresholds for potentiation and depression, we are able to demonstrate a memory-dependent induction of learning. The discharging of the quantum dot can further be induced by low-power light pulses in the nanowatt range. In combination with the state-dependent threshold voltage for discharging, this enables applications as generic building blocks to perform arithmetic operations in bases ranging from binary to decimal with low-power optical excitation. Our findings allow the realization of optoelectronic memristor-based synapses in artificial neural networks with a memory-dependent induction of learning and enhanced functionality by performing arithmetic operations.
Original language | English |
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Article number | 054011 |
Pages (from-to) | 1-9 |
Number of pages | 9 |
Journal | Physical Review Applied |
Volume | 5 |
Issue number | 5 |
DOIs | |
Publication status | Published - 17 May 2016 |
Keywords
- Memristor
- Artificial neural network
- Metaplasticity
- Synaptic plasticity
- Quantum dot
- Floating gate transistor