Demonstration of In-Memory Biosignal Analysis: Novel High-Density and Low-Power 3D Flash Memory Array for Arrhythmia Detection.

Smart healthcare systems integrated with advanced deep neural networks enable real-time health monitoring, early disease detection, and personalized treatment. In this work, a novel 3D AND-type flash memory array with a rounded double channel for computing-in-memory (CIM) architecture to overcome the limitations of conventional smart healthcare systems: the necessity of high area and energy efficiency while maintaining high classification accuracy is proposed. The fabricated array, characterized by low-power operations and high scalability with double independent channels per floor, exhibits enhanced cell density and energy efficiency while effectively emulating the features of biological synapses. The CIM architecture leveraging the fabricated array achieves high classification accuracy (93.5%) for electrocardiogram signals, ensuring timely detection of potentially life-threatening arrhythmias. Incorporated with a simplified spike-timing-dependent plasticity learning rule, the CIM architecture is suitable for robust, area- and energy-efficient in-memory arrhythmia detection systems. This work effectively addresses the challenges of conventional smart healthcare systems, paving the way for a more refined healthcare paradigm.

Unsupervised online learning of temporal information in spiking neural network using thin-film transistor-type NOR flash memory devices.

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.

Unsupervised Online Learning With Multiple Postsynaptic Neurons Based on Spike-Timing-Dependent Plasticity Using a Thin-Film Transistor-Type NOR Flash Memory Array.

We present a two-layer fully connected neuromorphic system based on a thin-film transistor (TFT)-type NOR flash memory array with multiple postsynaptic (POST) neurons. Unsupervised online learning by spike-timing-dependent plasticity (STDP) on the binary MNIST handwritten datasets is implemented, and its recognition result is determined by measuring firing rate of POST neurons. Using a proposed learning scheme, we investigate the impact of the number of POST neurons in terms of recognition rate. In this neuromorphic system, lateral inhibition function and homeostatic property are exploited for competitive learning of multiple POST neurons. The simulation results demonstrate unsupervised online learning of the full black-and-white MNIST handwritten digits by STDP, which indicates the performance of pattern recognition and classification without preprocessing of input patterns.

Grayscale Image Recognition Using Spike-Rate-Based Online Learning and Threshold Adjustment of Neurons in a Thin-Film Transistor-Type NOR Flash Memory Array.

As a synaptic device, TFT-type NOR flash memory cell shows reasonable weight levels (50 levels for long-term potentiation (LTP) and 150 levels for long-term depression (LTD)) and large max/min ratio (═50) for synapse weight. Based on the measurement results of the synapse cell, supervised learning process is simulated using software MATLAB. A new pulse scheme is designed for mimicking spike-rate-dependent plasticity (SRDP) algorithm. Through learning and inferencing phase, our (784 × 100) network achieved 74.08% accuracy on the MNIST benchmark. A new method for adapting the threshold voltage of output neurons for firing is also proposed. This additional adjustment helps to eliminate the exclusive or dormant output neurons by setting the threshold voltage to an appropriate value proportional to the average weight of synapses connected to each neuron. As a result, accuracy increases to 82.54% in the (784 × 100) network and to 84.14% in the (784 × 200) network. Moreover, threshold adjustment helped the network to classify completely overlapped patterns in succession.

Emerging memory technologies for neuromorphic computing.

In this paper, we reviewed the recent trends on neuromorphic computing using emerging memory technologies. Two representative learning algorithms used to implement a hardware-based 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.

A Split-Gate Positive Feedback Device With an Integrate-and-Fire Capability for a High-Density Low-Power Neuron Circuit.

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.

Smart Contact Lenses with Graphene Coating for Electromagnetic Interference Shielding and Dehydration Protection.

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.