Zezhong Yin

Electrolyte-gated transistors are promising candidates for neuromorphic devices due to their operational similarity to biological synapses. However, undesirable conductance update is caused by the uncontrollable migration of ions within the electrolyte, thereby limiting their applications in scenarios requiring precise and reliable learning. In this work, an indium-gallium-zinc oxide (IGZO) synaptic transistor was fabricated via a solution process, employing a Li+-doped ZrOx/lead zirconate titanate (PZT) composite as the gate dielectric layer. Ion migration is effectively modulated by the ferroelectric polarization effect of the PZT film, and the IGZO transistor is endowed with tunable synaptic plasticity. Various synaptic behaviors were emulated, including excitatory postsynaptic currents, paired-pulse facilitation, and learning-experience behavior. Based on the volatile and nonvolatile conductance characteristics of IGZO synaptic transistors, a 4-bit encoded reservoir computing system was developed and applied to pattern recognition tasks, effectively reducing the network scale without compromising accuracy. This work demonstrates a ferroelectric/electrolyte synergistically modulated synaptic transistor for the development of efficient and accurate neuromorphic computing.

With the increasing demand for processing massive and unstructured data, computing systems based on the von Neumann architecture are facing challenges of low-speed and high-energy consumption. Neuromorphic devices with synaptic functions are gradually emerging, which provides hardware support for the construction of brain-like computing systems. As an important branch of neuromorphic devices, synaptic transistors have shown great potential in energy-efficient parallel computing. Among the various types of synaptic transistors, oxide-based synaptic transistors (OSTs) have attracted widespread attention due to their compatibility with silicon technology and operating stability. Herein, the basic functionalities and the latest developments of OSTs are introduced. According to different operating mechanisms, OSTs are classified as electrolyte-gated synaptic transistors, ferroelectric synaptic transistors, charge trapping synaptic transistors, and photoelectric synaptic transistors. The material selection, device configuration, and synaptic characteristics of various devices are discussed. The application scenarios of OSTs in various fields are summarized. Finally, the development prospects of OSTs that could be significant for constructing neuromorphic systems are outlined.

Brain-inspired neuromorphic computing has been widely considered a promising solution to overcome the limitations of traditional von Neumann architecture in the current computer system. As an essential component of the neuromorphic system, the artificial synaptic device exhibits great potential in adaptive learning. Due to their controllable channel conductance and CMOS compatibility, solid electrolyte-gated synaptic transistors (EGSTs) have garnered significant interest as next-generation neuromorphic devices. However, most of the existing EGSTs suffer from rapid self-diffusion of the ions, making it difficult to maintain the stable channel conductance states. In this work, the synaptic transistors were fabricated with indium–gallium–zinc oxide as the channel layer, Al2O3 as the charge trapping layer, and ZrO2 as the solid electrolyte layer. The self-diffusion of the hydrogen ions can be suppressed by the positive charges trapped in the Al2O3 layer, which significantly improves the long-term plasticity (LTP) of the devices. By adjusting the presynaptic spike scheme, the typical synaptic behaviors, including excitatory postsynaptic current, paired-pulse facilitation, and the transition from short-term memory to long-term memory, were simulated. Based on the conductance modulation properties of the channel in the synaptic transistor, an artificial neural network was constructed for pattern recognition, and a high accuracy of 95.4% was obtained. This work demonstrates an effective strategy for the enhancement of the LTP of the synaptic transistor.

Photoelectric synaptic transistors (PSTs) based on metal oxide semiconductors (MOSs) have shown promising applications in visual perception and photonic computing. However, the response range of the PST is limited in the ultra-violet region due to the wide bandgap of the MOS. Herein, a visible light-driven InGaZnO PST based on CeOx floating gate is presented. The optical response of the PST is improved due to the introduction of oxygen vacancies in the CeOx floating gate, and the tunable synaptic characteristics are endowed. Various synaptic behaviors under visible light stimulation have been simulated, including paired-pulse facilitation, high-pass filtering characteristics, the transition from short-term memory to long-term memory, and learning-experience behavior. The multilevel conductance modulation is realized through optical programming and electrical erasing operations. An artificial neural network was constructed based on the long-term plasticity of the PST, and 95.3% accuracy was achieved in image recognition. This work promotes the development of oxide-based PST and provides a candidate for artificial visual bionics inspired by visible light.

The development of photoelectric synaptic transistors (PSTs) using visible light-driven mimicking synaptic behaviors represents a key advancement toward biomimetic visual systems. This study proposes a PST based on bilayer indium-gallium-zinc-oxide (IGZO) homojunctions with tunable gallium ratios. By optimizing the gallium content, oxygen vacancy concentrations in the channel were precisely controlled, suppressing deionization processes and enhancing device performance. The IGZO homojunction PST demonstrated outstanding electrical characteristics (Ion/Ioff = 1.2 × 107, μ = 3.88 cm2/Vs, Vth = 0 V) and exhibited high photocurrent and robust persistent photoconductivity under visible light. The device mimicked various synaptic behaviors, including excitatory postsynaptic current, paired-pulse facilitation, the transition from short-term plasticity to long-term plasticity, spiking-rate-dependent plasticity, and spike-timing-dependent plasticity. Furthermore, leveraging the potentiation and depression behaviors of the IGZO homojunction PST, a triple-layer neural network achieved 96.8% accuracy in pattern recognition tasks. These results underscore the IGZO homojunction PST's immense potential for advancing artificial vision systems.

Field-effect transistors (FETs) based on indium oxide (In2O3) nanofibers demonstrate significant potential for applications in next-generation electronic devices. However, In2O3 nanofiber FETs typically exhibit deteriorated electrical performance and bias stability due to the disordered arrangement of nanofibers and a high concentration of oxygen vacancy defects. In this study, In2O3 nanofibers were prepared by electrospinning, and the effects of nanofiber orientation and Y2O3 passivation on FET electrical performance were systematically investigated. The results indicate that after Y2O3 passivation, the aligned In2O3 nanofiber FETs exhibit enhanced electrical performance and superior positive bias stress and negative bias illumination stress stability. The Y2O3 passivation layer effectively prevents the penetration of external O2 and H2O molecules, while the diffusion of Y3+ into the back channel reduces oxygen vacancies, thereby improving device stability. When Al2O3 was employed as the dielectric layer, the electrical performance of aligned In2O3 nanofiber FET with Y2O3 passivation was further optimized, achieving a mobility of 18.2 cm2/V s and a subthreshold swing of 85 mV/dec. Meanwhile, the FET exhibits excellent environmental stability after 60 days of atmospheric exposure. This work provides a strategy for fabricating nanofiber-based FETs with high mobility and stability.