Retinomorphic systems that can see, recognize, and respond to real-time environmental information will extend the complexity and range of tasks that an exoskeleton robot can perform to better assist physically disabled people. However, the lack of ultrasensitive, reconfigurable, and large-scale integratable retinomorphic devices and advanced edge-processing algorithms makes it difficult to realize retinomorphic hardware. Here, we report the retinomorphic hardware prototype with a 4096-pixel perovskite image sensor array as core module to endow embodied intelligent vision functionalities. The retinomorphic sensor array, using a one photodetector–one transistor geometry to resemble retinal circuit with broadband, ultrahigh, and reconfigurable photoresponsivities, executes both adaptive imaging with a contrast enhancement of ~620% under a dim-lit intensity of 10 microwatts per square centimeter and an instantaneous one-dimensional feature extraction algorithm to decompose the origin visual scenarios into parsimoniously encoded spatiotemporal information. This retinomorphic system endows embodied intelligence with adaptive imaging, in situ processing, and decision-making capabilities and promises enormous potential for autonomous robot applications.

High‐performance artificial synapse with nonvolatile memory and low power consumption is a perfect candidate for brainoid intelligence. Unfortunately, due to the energy barrier paradox between ultra‐low power and nonvolatile modulation of device conductances, it is still a challenge at the moment to construct such ideal synapses. Herein, a proton‐reservoir type 4,4′,4″,4'''‐(Porphine‐5,10,15,20‐tetrayl) tetrakis (benzenesulfonic acid) (TPPS) molecule and fabricated organic protonic memristors with device width of 10 µm to 100 nm is synthesized. The occurrence of sequential proton migration and interfacial self‐coordinated doping will introduce new energy levels into the molecular bandgap, resulting in effective and nonvolatile modulation of device conductance over 64 continuous states with retention exceeding 30 min. The power consumptions of modulating and reading the device conductance approach the zero‐power operating limits, which range from 16.25 pW to 2.06 nW and 6.5 fW to 0.83 pW, respectively. Finally, a robust artificial synapse is successfully demonstrated, showing spiking‐rate‐dependent plasticity (SRDP) and spiking‐timing‐dependent plasticity (STDP) characteristics with ultra‐low power of 0.66 to 0.82 pW, as well as 100 long‐term depression (LTD)/potentiation (LTP) cycles with 0.14%/0.30% weight variations.

In-sensor computing can simultaneously output image information and recognition results through in-situ visual signal processing, which can greatly improve the efficiency of machine vision. However, in-sensor computing is challenging due to the requirement to controllably adjust the sensor’s photosensitivity. Herein, it is demonstrated a ternary cationic halide Cs_0.05FA_0.81MA_0.14 Pb(I_0.85Br_0.15)₃ (CsFAMA) perovskite, whose External quantum efficiency (EQE) value is above 80% in the entire visible region (400–750 nm), and peak responsibility value at 750 nm reaches 0.45 A/W. In addition, the device can achieve a 50-fold enhancement of the photoresponsibility under the same illumination by adjusting the internal ion migration and readout voltage. A proof-of-concept visually enhanced neural network system is demonstrated through the switchable photosensitivity of the perovskite sensor array, which can simultaneously optimize imaging and recognition results and improve object recognition accuracy by 17% in low-light environments.

The hierarchically coordinated processing of visual information with the data degradation characteristic embodies the energy consumption minimization and signal transmission efficiency maximization of brain activities. This inspires machine vision to handle the explosively increased data in real-time. In this contribution, we demonstrate the possibility of constructing a coordinated perceptive computing paradigm with dual-mode organic memristors to emulate the visual processing capability of the brain systems. The 32-state modulation of the device photoresponsivity and conductance via photo-induced molecular reconfiguration and electrochemical redox activities enables the execution of computing-in-sensor and computing-in-memory tasks, respectively, which in turn allows the homogeneous hardware integration of a single-layer perceptron and a convolutional neural network for high-efficiency hierarchical visual processing. Compared to the sole optoelectronic CIS mode to recognize visual targets, the dual-mode organic memristor-based coordinated computing scheme demonstrates a 24.5% improvement in the recognition accuracy and 45.8% reduction in the network size.

The intrinsic instability of non-fullerene acceptors (NFAs), primarily caused by vulnerable exocyclic vinyl linkages, significantly undermines the operational lifetime of organic solar cells (OSCs). In this work, we address this challenge by designing robust molecular framework that incorporates fluoroacetonitrile-containing terminal groups. This terminal group enables the formation of a F···H interaction between itself and the exocyclic vinyl linkage. Leveraging this F···H interaction, we can readily synthesize the preferable E isomer with exclusive regioselectivity. Moreover, the F···H interaction locks the molecular conformation, resulting in a more planar structure, and thereby enhancing molecular packing and film morphology. Critically, this interaction stabilizes the exocyclic vinyl linkage, significantly boosting the chemical and photo-stability and suppressing terminal-group redistribution of the NFAs. When paired with the low-cost polymer donor P3HT, the symmetric acceptor E-2IFC-F achieves a power conversion efficiency (PCE) of 11.77%, setting a high benchmark for P3HT-based OSCs. Furthermore, the asymmetric acceptor E-IFC-IC-Cl, with its optimized energy levels, delivers a PCE of 19.31% when combined with the donor PM6. All of the devices based on these NFAs exhibit better stability. This work presents a paradigm for developing high-efficiency, stable, and low-cost OSCs, highlighting the potential of fluoroacetonitrile-containing terminal groups in enhancing NFA performance.