Neuromorphic computing, which mimics the energy-efficient parallel processing capabilities of the human brain, has emerged as an alternative to traditional von Neumann architectures that struggle with high power consumption in the era of artificial intelligence (AI). Despite the potential of Si-based neuromorphic chips, they often face fundamental limitations in integration density and biological compatibility, necessitating the development of next-generation devices that can better emulate the ionic signaling of biological systems. This review provides a comprehensive analysis of the recent research trends in artificial synapses and neurons based on organic electrochemical transistors (OECTs), highlighting their unique ability to achieve high transconductance and mixed ionic-electronic conduction at ultra-low operating voltages. We discuss how OECTs successfully replicate diverse synaptic plasticities and complex neuronal spiking behaviors through advanced material engineering and structural optimizations such as vertical architectures. Furthermore, this review discusses the implementation of high-order neural functions, including associative learning and logic operations, which are facilitated by the inherent electrochemical dynamics of organic semiconductors. Finally, overcoming current challenges in reliability and scalability will establish OECTs as a pivotal platform for low-power neuromorphic hardware and bio-integrated electronics.

Neuromorphic computing, inspired by the biological mechanisms of neural signal transmission, has emerged as a promising technology for efficient and parallel data processing with minimal power consumption. In this study, we developed floating-gate organic thin-film transistors (OTFTs) with self-assembled monolayer (SAM)-based tunneling layers to mimic the characteristics of artificial synapses. The tunneling layers were formed using mixed phosphonic acid SAMs with varying ratios of octadecylphosphonic acid (ODPA) and 12-pentafluorophenoxydodecylphosphonic acid (PFPA). The influence of these ratios on the memory and neuromorphic characteristics of the devices was systematically evaluated. Our results revealed that the ODPA ratio significantly impacts the hysteresis window, with higher ODPA content yielding improved memory characteristics. Conversely, the PFPA : ODPA ratio of 2:1 exhibited the lowest non-linearity (NL = 0.48), demonstrating the potential for highly accurate weight updates in neuromorphic devices. Additionally, pulse width modulation studies showed that a pulse width of 100 ms optimized the linearity and stability of long-term potentiation (LTP) and depression (LTD) characteristics. The combination of sol-gel processed AlOx as a floating-gate layer and tailored SAM-based tunneling layers allowed for precise control of device performance. These findings highlight the importance of molecular engineering in designing SAM layers to balance memory retention and neuromorphic functionality. This study provides a pathway for advancing organic floating-gate transistors as a core component in next-generation neuromorphic computing systems.

The report reviews recent research efforts in demonstrating a computing system whose operation principle mimics the dynamics of biological neurons. The temporal variation of the membrane potential of neurons is one of the key features that contribute to the information processing in the brain. We first summarize the neuron models that explain the experimentally observed change in the membrane potential. The function of ion channels is briefly introduced to understand such change from the molecular viewpoint. Dedicated circuits that can simulate the neuronal dynamics have been developed to reproduce the charging and discharging dynamics of neurons depending on the input ionic current from presynaptic neurons. Key elements include volatile memristors that can undergo volatile resistance switching depending on the voltage bias. This behavior called the threshold switching has been utilized to reproduce the spikes observed in the biological neurons. Various types of threshold switch have been applied in a different configuration in the hardware demonstration of neurons. Recent studies revealed that the memristor-based circuits could provide energy and space efficient options for the demonstration of neurons using the innate physical properties of materials compared to the options demonstrated with the conventional complementary metal-oxidesemiconductors (CMOS).

Artificial neuromorphic devices are considered the key component in realizing energy-efficient and brain-inspired computing systems. For the artificial neuromorphic devices, various material candidates and device architectures have been reported, including two-dimensional materials, metal-oxide semiconductors, organic semiconductors, and halide perovskite materials. In addition to conventional electrical neuromorphic devices, optoelectronic neuromorphic devices, which operate under a light stimulus, have received significant interest due to their potential advantages such as low power consumption, parallel processing, and high bandwidth. This article reviews the recent progress in optoelectronic neuromorphic devices using various active materials such as two-dimensional materials, metal-oxide semiconductors, organic semiconductors, and halide perovskites