This paper presents a neuromorphic system for visual pattern recognition realized in hardware. A new learning rule based on modified spike-timing-dependent plasticity is also presented and implemented with passive synaptic devices. The system includes an artificial photoreceptor, a Pr _0.7 Ca _0.3 MnO ₃ -based memristor array, and CMOS neurons. The artificial photoreceptor consisting of a CMOS image sensor and a field-programmable gate array converts an image into spike signals, and the memristor array is used to adjust the synaptic weights between the input and output neurons according to the learning rule. A leaky integrate-and-fire model is used for the output neuron that is built together with the image sensor on a single chip. The system has 30 input neurons that are interconnected to 10 output neurons through 300 memristors. Each input neuron corresponding to a pixel in a 5 × 6 pixel image generates voltage pulses according to the pixel value. The voltage pulses are then weighted and integrated by the memristors and the output neurons, respectively, to be compared with a certain threshold voltage above which an output neuron fires. The system has been successfully demonstrated by training and recognizing number images from 0 to 9.

Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks. Despite significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns. In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics. The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes and has been successfully adapted to a neural network system. The system learns and later recognizes, the human thought pattern corresponding to three vowels, i.e. /a /, /i / and /u/, using electroencephalography signals generated while a subject imagines speaking vowels. Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction.

In this study, a circuit technique and training algorithm that minimizes the effect of stuck-at-faults (SAFs) within a memristor crossbar array of neural networks (NNs) are presented. To improve the network performance in the presence of SAFs, a conventional transimpedance amplifier, which is used for summing the currents that flow through the memristors, is modified to ensure that the amplifier output is within the appropriate operating range. Further improvement in the network performance is achieved by using the proposed training algorithm, which utilizes the locations and values of faulty memristors for network training. A feedforward NN employing 32 x 32 memristor crossbar arrays is implemented to verify the performance improvement in the NNs using the proposed circuit technique and training algorithm.

This letter presents an investigation of analog synapse characteristics of a PCMO-based interface switching device with varying electrode materials. In comparison with the filamentary switching device having only 1-b storage and variability issues, the interface switching devices exhibit excellent electrical properties, such as 5-b (32-level) multi-level cell characteristics, wafer-scale switching uniformity, and scalability of the switching energy with device area. To improve data retention of the interface switching device, we propose a Mo electrode to increase the oxidation barrier height (~0.4 eV) that, in turn, significantly improves the retention time and pattern classification accuracy of neural networks.

We report oxide based analog synpase for neuromorphic system. By optimizing redox reaction at the metal/oxide interface, we can obtain stable analog synapse characteristics and wafer scale switching uniformity. We have confirmed the feasibility of neuromorphic hardware system with oxide synapse array device which recognizes the electroencephalogram (EEG) signal and rat's neural signal.

We report novel nanoscale synapse and neuron devices for ultra-high density neuromorphic system. By adopting a Mo electrode, the redox reaction at Mo/Pr_0.7Ca_0.3MnO₃ (PCMO) interface was controlled which in turn significantly improve synapse characteristics such as switching uniformity, disturbance, retention and multi-level data storage under identical pulse condition. Furthermore, The NbO₂ based Insulator-Metal Transition (IMT) oscillator was developed for neuron application. Finally, we have experimentally confirmed the realization of pattern recognition with high accuracy using the 11k-bit Mo/PCMO synapse array and NbO₂ oscillator neuron.