Recently, smart contact lenses with electronic circuits have been proposed for various sensor and display applications where the use of flexible and biologically stable electrode materials is essential. Graphene is an atomically thin carbon material with a two-dimensional hexagonal lattice that shows outstanding electrical and mechanical properties as well as excellent biocompatibility. In addition, graphene is capable of protecting eyes from electromagnectic (EM) waves that may cause eye diseases such as cataracts. Here, we report a graphene-based highly conducting contact lens platform that reduces the exposure to EM waves and dehydration. The sheet resistance of the graphene on the contact lens is as low as 593 Ω/sq (±9.3%), which persists in an wet environment. The EM wave shielding function of the graphene-coated contact lens was tested on egg whites exposed to strong EM waves inside a microwave oven. The results show that the EM energy is absorbed by graphene and dissipated in the form of thermal radiation so that the damage on the egg whites can be minimized. We also demonstrated the enhanced dehydration protection effect of the graphene-coated lens by monitoring the change in water evaporation rate from the vial capped with the contact lens. Thus, we believe that the graphene-coated contact lens would provide a healthcare and bionic platform for wearable technologies in the future.

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In this paper, we reviewed the recent trends on neuromorphic computing using emerging memory technologies. Two representative learning algorithms used to implement a hardwarebased neural network are described as a bio-inspired learning algorithm and software-based learning algorithm, in particular back-propagation. The requirements of the synaptic device to apply each algorithm were analyzed. Then, we reviewed the research trends of synaptic devices to implement an artificial neural network.

Hardware-based spiking neural networks (SNNs) to mimic biological neurons have been reported. However, conventional neuron circuits in SNNs have a large area and high power consumption. In this work, a split-gate floating-body positive feedback (PF) device with a charge trapping capability is proposed as a new neuron device that imitates the integrate-and-fire function. Because of the PF characteristic, the subthreshold swing (SS) of the device is less than 0.04 mV/dec. The super-steep SS of the device leads to a low energy consumption of ∼0.25 pJ/spike for a neuron circuit (PF neuron) with the PF device, which is ∼100 times smaller than that of a conventional neuron circuit. The charge storage properties of the device mimic the integrate function of biological neurons without a large membrane capacitor, reducing the PF neuron area by about 17 times compared to that of a conventional neuron. We demonstrate the successful operation of a dense multiple PF neuron system with reset and lateral inhibition using a common self-controller in a neuron layer through simulation. With the multiple PF neuron system and the synapse array, on-line unsupervised pattern learning and recognition are successfully performed to demonstrate the feasibility of our PF device in a neural network.

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Hardware-based Spiking Neural Networks (SNNs) are regarded as promising candidates for the cognitive computing system due to its low power consumption and highly parallel operation. In this paper, we train the SNN in which the firing time carries information using temporal backpropagation. The temporally encoded SNN with 512 hidden neurons achieved an accuracy of 96.90% for the MNIST test set. Furthermore, the effect of the device variation on the accuracy in temporally encoded SNN is investigated and compared with that of the rate-encoded network. In a hardware configuration of our SNN, NOR-type analog memory having an asymmetric floating gate is used as a synaptic device. In addition, we propose a neuron circuit including a refractory period generator for temporally encoded SNN. The performance of the 2-layer neural network composed of synapses and proposed neurons is evaluated through circuit simulation using SPICE based on the BSIM3v3 model with 0.35 μm technology. The network with 128 hidden neurons achieved an accuracy of 94.9%, a 0.1% reduction compared to that of the system simulation of the MNIST dataset. Finally, each block's latency and power consumption constituting the temporal network is analyzed and compared with those of the rate-encoded network depending on the total time step. Assuming that the network has 256 total time steps, the temporal network consumes 15.12 times less power than the rateencoded network and makes decisions 5.68 times faster.

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Brain-inspired analog neuromorphic systems based on the synaptic arrays have attracted large attention due to low-power computing. Spike-timing-dependent plasticity (STDP) algorithm is considered as one of the appropriate neuro-inspired techniques to be applied for on-chip learning. The aim of this study is to investigate the methodology of unsupervised STDP based learning in temporal encoding systems. The system-level simulation was performed based on the measurement results of thin-film transistor-type asymmetric floating-gate NOR flash memory. With proposed learning methods, 91.53% of recognition accuracy is obtained in inferencing MNIST standard dataset with 200 output neurons. Moreover, temporal encoding rules showed that the number of input pulses and the computing power can be compressed without significant loss of recognition accuracy compared to the conventional rate encoding scheme. In addition, temporal computing in a multi-layer network is suitable for learning data sequences, suggesting the possibility of applying to real-world tasks such as classifying direction of moving objects.