Interface Charge Engineering in Ferroelectric Neuristors for a Complete Machine Vision System.

The rapid advancement of artificial intelligence has driven the demand for hardware solutions of neuromorphic pathways to effectively mimic biological functions of the human visual system. However, current machine vision systems (MVSs) fail to fully replicate retinal functions and lack the ability to update weights through all-optical pulses. Here, by employing rational interface charge engineering via varying the charge trapping layer thickness of PMMA, we determine that the ferroelectric polarization of our ferroelectric neuristors can be flexibly manipulated through light or electrical pulses.

IGZO/PVP Composite Nanofiber Neuromorphic Transistors with Optoelectronic Synapse Emulation and Reservoir Computing.

Nanofiber neuromorphic transistors are regarded as promising candidates for mimicking brain-like learning and advancing high-performance computing. Composite nanofibers (CNFs) typically exhibit enhanced optoelectronic and mechanical properties. In this study, indium-gallium-zinc oxide (IGZO)/polyvinylpyrrolidone (PVP) CNFs were synthesized, and the neuromorphic transistor was integrated on both rigid and flexible substrates.

Energy-Efficient Reservoir Computing Based on Solution-Processed Electrolyte/Ferroelectric Memcapacitive Synapses for Biosignal Classification.

The classification of critical physiological signals using neuromorphic devices is essential for early disease detection. Physical reservoir computing (RC), a lightweight temporal processing neural network, offers a promising solution for low-power, resource-constrained hardware. Although solution-processed memcapacitive reservoirs have the potential to improve power efficiency as a result of their ultralow static power consumption, further advancements in synaptic tunability and reservoir states are imperative to enhance the capabilities of RC systems.

Toward a Brain-Neuromorphics Interface.

Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries.

Power-Efficient Multisensory Reservoir Computing Based on Zr-Doped HfO Memcapacitive Synapse Arrays.

Hardware implementation tailored to requirements in reservoir computing would facilitate lightweight and powerful temporal processing. Capacitive reservoirs would boost power efficiency due to their ultralow static power consumption but have not been experimentally exploited yet. Here, this work reports an oxide-based memcapacitive synapse (OMC) based on Zr-doped HfO (HZO) for a power-efficient and multisensory processing reservoir computing system.

Integration of image preprocessing and recognition functions in an optoelectronic coupling organic ferroelectric retinomorphic neuristor.

The human visual system (HVS) has the advantages of a low power consumption and high efficiency because of the synchronous perception and early preprocessing of external image information in the retina, as well as parallel in-memory computing within the visual cortex. Realizing the biofunction simulation of the retina and visual cortex in a single device structure provides opportunities for performance improvements and machine vision system (MVS) integration. Here, we fabricate organic ferroelectric retinomorphic neuristors that integrate the retina-like preprocessing function and recognition of the visual cortex in a single device architecture.

Molecular-Layer-Defined Asymmetric Schottky Contacts in Organic Planar Diodes for Self-Powered Optoelectronic Synapses.

Optoelectronic synapses have been utilized as neuromorphic vision sensors for image preprocessing in artificial visual systems. Self-powered optoelectronic synapses, which can directly convert optical power into electrical power, are promising for practical applications. The Schottky junction tends to be a promising candidate as the energy source for electrical operations.

A Smarter Pavlovian Dog with Optically Modulated Associative Learning in an Organic Ferroelectric Neuromem.

Associative learning is a critical learning principle uniting discrete ideas and percepts to improve individuals' adaptability. However, enabling high tunability of the association processes as in biological counterparts and thus integration of multiple signals from the environment, ideally in a single device, is challenging. Here, we fabricate an organic ferroelectric neuromem capable of monadically implementing optically modulated associative learning.

pJ-Level Energy-Consuming, Low-Voltage Ferroelectric Organic Field-Effect Transistor Memories.

Ferroelectric organic field-effect transistors (Fe-OFETs) have attracted considerable attention because of their promising potential for memory applications, while a critical issue is the large energy consumption mainly caused by a high operating voltage and slow data switching. Here, we employ ultrathin ferroelectric polymer and semiconducting molecular crystals to create low-voltage Fe-OFET memories. Devices require only pJ-level energy consumption.