Resistive switching devices were used as technological synapses to learn about the spatial- and temporal-correlated neuron spikes.

Resistive switching memory (RRAM) has been proposed as 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 state-of-the-art bistable RRAM and to introduce small-area building blocks serving as artificial synapses. This work introduces a new synaptic circuit consisting of a one-transistor/one-resistor (1T1R) structure, where the resistive element is a HfO2 RRAM with bipolar switching. The spiketiming dependent plasticity (STDP) 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.

Brain-inspired computation can revolutionize information technology by introducing machines capable of recognizing patterns (images, speech, video) and interacting with the external world in a cognitive, humanlike way. Achieving this goal requires first to gain a detailed understanding of the brain operation, and second to identify a scalable microelectronic technology capable of reproducing some of the inherent functions of the human brain, such as the high synaptic connectivity (~104) and the peculiar time-dependent synaptic plasticity. Here we demonstrate unsupervised learning and tracking in a spiking neural network with memristive synapses, where synaptic weights are updated via brain-inspired spike timing dependent plasticity (STDP). The synaptic conductance is updated by the local time-dependent superposition of pre- and post-synaptic spikes within a hybrid one-transistor/one-resistor (1T1R) memristive synapse. Only 2 synaptic states, namely the low resistance state (LRS) and the high resistance state (HRS), are sufficient to learn and recognize patterns. Unsupervised learning of a static pattern and tracking of a dynamic pattern of up to 4 × 4 pixels are demonstrated, paving the way for intelligent hardware technology with up-scaled memristive neural networks.

Resistive switching memory (RRAM) is currently under consideration for fast nonvolatile memory thanks to its relatively low cost and high performance. A key concern for RRAM reliability is stochastic switching, which impacts the operation of the digital memory due to distribution broadening. On the other hand, stochastic behaviors are enabling mechanisms for some computing tasks, such as physical unclonable functions (PUF) and random number generation (RNG). Here we present new circuit blocks for physical RNG, based on the coupling of 2 RRAM devices. The 2-resistance (2R) scheme allows to overcome the need of probability tracking, where the operation voltage must be tuned to adjust the generation probabilities of 0 and 1. Probability tests are proven successful for one of the 3 proposed schemes.

Neural networks with resistive-switching memory (RRAM) synapses can mimic learning and recognition in the human brain, thus overcoming the major limitations of von Neumann computing architectures. While most researchers aim at supervised learning of a pre-determined set of patterns, unsupervised learning of patterns might be attractive for brain-inspired robot/drone navigation. Here we demonstrate neural networks with CMOS/RRAM synapses capable of unsupervised learning by spike-time dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP). First, STDP learning in a RRAM synaptic network is demonstrated. Then we present a 4-transistor/1-resistor synapse capable of SRDP, finally demonstrating SRDP learning, update, and recognition of patterns at the level of neural network