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Cointegration of multistate single-transistor neurons and synapses was demonstrated for highly scalable neuromorphic hardware, using nanoscale complementary metal-oxide semiconductor (CMOS) fabrication. The neurons and synapses were integrated on the same plane with the same process because they have the same structure of a metal-oxide semiconductor field-effect transistor with different functions such as homotype. By virtue of 100% CMOS compatibility, it was also realized to cointegrate the neurons and synapses with additional CMOS circuits. Such cointegration can enhance packing density, reduce chip cost, and simplify fabrication procedures. The multistate single-transistor neuron that can control neuronal inhibition and the firing threshold voltage was achieved for an energy-efficient and reliable neural network. Spatiotemporal neuronal functionalities are demonstrated with fabricated single-transistor neurons and synapses. Image processing for letter pattern recognition and face image recognition is performed using experimental-based neuromorphic simulation.

A neuromorphic module of an electronic nose (E-nose) is demonstrated by hybridizing a chemoresistive gas sensor made of a semiconductor metal oxide (SMO) and a single transistor neuron (1T-neuron) made of a metal-oxide-semiconductor field-effect transistor (MOSFET). By mimicking a biological olfactory neuron, it simultaneously detects a gas and encoded spike signals for in-sensor neuromorphic functioning. It identifies an odor source by analyzing the complicated mixed signals using a spiking neural network (SNN). The proposed E-nose does not require conversion circuits, which are essential for processing the sensory signals between the sensor array and processors in the conventional bulky E-nose. In addition, they do not have to include a central processing unit (CPU) and memory, which are required for von Neumann computing. The spike transmission of the biological olfactory system, which is known to be the main factor for reducing power consumption, is realized with the SNN for power savings compared to the conventional E-nose with a deep neural network (DNN). Therefore, the proposed neuromorphic E-nose is promising for application to Internet of Things (IoT), which demands a highly scalable and energy-efficient system. As a practical example, it is employed as an electronic sommelier by classifying different types of wines.

Realizing a neuromorphic-based artificial visual system with low-cost hardware requires a neuromorphic device that can react to light stimuli. This study introduces a photoresponsive neuron device composed of a single transistor, developed by engineering an artificial neuron that responds to light, just like retinal neurons. Neuron firing is activated primarily by electrical stimuli such as current via a well-known single transistor latch phenomenon. Its firing characteristics, represented by spiking frequency and amplitude, are additionally modulated by optical stimuli such as photons. When light is illuminated onto the neuron transistor, electron−hole pairs are generated, and they allow the neuron transistor to fire at lower firing threshold voltage. Different photoresponsive properties can be modulated by the intensity and wavelength of the light, analogous to the behavior of retinal neurons. The artificial visual system can be miniaturized because a photoresponsive neuronal function is realized without bulky components such as image sensors and extra circuits.

A spiking neural network (SNN) inspired by the structure and principles of the human brain can significantly enhance the energy efficiency of artificial intelligence computing by overcoming the bottlenecks of the conventional von Neumann architecture with its massive parallelism and spike transmissions. The construction of artificial neurons is important for the hardware implementation of an SNN, which generates spike signals when enough synaptic signals are gathered. Because circuit‐level artificial neurons with comparator and reset circuits require considerable hardware area, intensive efforts are devoted in recent years for building artificial neurons at the device level for better area efficiency. Furthermore, artificial sensory neuron devices, which perform neural processing and sensing concurrently, have recently been developed in order to reduce the hardware cost and energy consumption of traditional sensory systems through in‐sensor computing. This review article surveys and benchmarks the recent progress of artificial neuron devices for neural processing and sensing. First, various artificial neuron devices are summarized, including single‐transistor neurons (1T‐neurons), memristor neurons, phase‐change neurons, magnetic neurons, and ferroelectric neurons. Next, cointegration technologies with artificial synaptic devices and artificial sensory neurons for in‐sensor computing are introduced. Finally, the challenges and prospects for developing artificial neuron devices are discussed.

Neuromorphic hardware computing is a promising alternative to von Neumann computing by virtue of its parallel computation, and low power consumption. To implement neuromorphic hardware based on deep neural network (DNN), a number of synaptic devices should be interconnected with neuron devices. For ideal hardware DNN, not only scalability and low power consumption, but also a linear and symmetric conductance change with the large number of conductance levels are required. Here an all-solid-state polymer electrolyte-gated synaptic transistor (pEGST) was fabricated on an entire silicon wafer with CMOS microfabrication and initiated chemical vapor deposition (iCVD) process. The pEGST showed good linearity as well as symmetry in potentiation and depression, conductance levels up to 8,192, and low switching energy smaller than 20 fJ/pulse. Selected 128 levels from 8,192 used to identify handwritten digits in the MNIST database with the aid of a multilayer perceptron, resulting in a recognition rate of 91.7 %.

A self‐powered artificial mechanoreceptor module is demonstrated with a triboelectric nanogenerator (TENG) as a pressure sensor with sustainable energy harvesting and a biristor as a neuron. By mimicking a biological mechanoreceptor, it simultaneously detects the pressure and encodes spike signals to act as an input neuron of a spiking neural network (SNN). A self‐powered neuromorphic tactile system composed of artificial mechanoreceptor modules with an energy harvester can greatly reduce the power consumption compared to the conventional tactile system based on von Neumann computing, as the artificial mechanoreceptor module itself does not demand an external energy source and information is transmitted with spikes in a SNN. In addition, the system can detect low pressures near 3 kPa due to the high output range of the TENG. It therefore can be advantageously applied to robotics, prosthetics, and medical and healthcare devices, which demand low energy consumption and low‐pressure detection levels. For practical applications of the neuromorphic tactile system, classification of handwritten digits is demonstrated with a software‐based simulation. Furthermore, a fully hardware‐based breath‐monitoring system is implemented using artificial mechanoreceptor modules capable of detecting wind pressure of exhalation in the case of pulmonary respiration and bending pressure in the case of abdominal breathing.