Neuromorphic Learning and Recognition With One-Transistor-One-Resistor Synapses and Bistable Metal Oxide RRAM

作者：Ambrogio, Stefano^*; Balatti, Simone^*; Milo, Valerio^*; Carboni, Roberto^*; Wang, Zhong-Qiang^*; Calderoni, Alessandro^*; Ramaswamy, Nirmal^*; Ielmini, Daniele^*

来源：IEEE Transactions on Electron Devices, 2016, 63(4): 1508-1515.

DOI：10.1109/TED.2016.2526647

• Artificial synapse
• memristive device
• neuromorphic network
• pattern learning
• resistive switching memory (RRAM)

Resistive switching memory (RRAM) has been proposed as an artificial synapse in neuromorphic circuits due to its tunable resistance, low power operation, and scalability. For the development of high-density neuromorphic circuits, it is essential to validate the state-of-the-art bistable RRAM and to introduce small-area building blocks serving as artificial synapses. This paper introduces a new synaptic circuit consisting of a one-transistor/one-resistor structure, where the resistive element is a HfO2 RRAM with bipolar switching. The spike-timing-dependent plasticity is demonstrated in both the deterministic and stochastic regimes of the RRAM. Finally, a fully connected neuromorphic network is simulated showing online unsupervised pattern learning and recognition for various voltages of the POST spike. The results support bistable RRAM for high-performance artificial synapses in neuromorphic circuits.

• 出版日期2016-4