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Si-based complementary metal-oxide-semiconductor (CMOS) transistors for logic computing have represented the most essential foundation of digital electronic technologies for decades toward the modern information era. The continuous scaling down of the transistor feature size has promoted significant improvements in the computing performance while gradually tending to its limit. Ubiquitous intelligent technologies have quickly penetrated daily life, yielding a tremendous increase in highly data-centric computing applications. Hence, emerging logic devices extending and even transcending the existing CMOS technology are urgently needed to meet the rapidly growing demand for information processing capability, involving revolutionary innovations from material science and architecture design to device applications. This thus gives us the opportunity to realize logic devices for state-of-the-art computing that are fundamentally far beyond the current devices. In this Perspective, we discuss the recent innovative design strategies of emerging logic devices along with the opportunities and challenges, providing a promising avenue toward high-performance and diversiform logic computing in the post-Moore era.

Molecular diodes are of considerable interest for the increasing technical demands of device miniaturization. However, the molecular diode performance remains contact-limited, which represents a major challenge for advancement of rectification ratio toward commercial availability. Here, we demonstrate that high-quality ultrathin organic semiconductor can be grown on several classes of metal substrates via solution-shearing epitaxy, with well-controlled number of layers and monolayer single crystal over 1 mm. The crystalline films are atomically smooth and free pinhole, providing a native interface for high-performance molecular diodes. As a result, the molecular diodes show record-high rectification ratio up to 5 × 108, ideality factor close to unity, aggressive unit conductance over 103 S/cm2, reverse breakdown electric field～1.1 × 108 V/cm, excellent electrical stability and well-defined contact interface. We further fabricate large-area molecular diode arrays with 100% yield and excellent uniformity in the diode metrics. Our results suggest that monolayer crystalline films have great potential to build reliable, high-performance molecular diodes and deeply understand their intrinsic electronic behavior.

Toward the development of effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by an electroencephalogram (EEG) is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To fill the gap, a novel deep learning (DL) framework based on the graph convolutional neural networks (GCNs) is presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes. Based on the absolute Pearson's matrix of overall signals, the graph Laplacian of EEG electrodes is built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features. The followed pooling layers reduce dimensionality, and the fully-connected (FC) softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and groupwise predictions. It has achieved the highest averaged accuracy, 93.06% and 88.57% (PhysioNet dataset), 96.24% and 80.89% (high gamma dataset), at the subject and group level, respectively, compared with existing studies, which suggests adaptability and robustness to individual variability. Moreover, the performance is stably reproducible among repetitive experiments for cross-validation. The excellent performance of our method has shown that it is an important step toward better BCI approaches. To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain MI. A DL library for EEG task classification including the code for this study is open source at https://github.com/SuperBruceJia/ EEG-DL for scientific research.

Aberrant hedgehog (Hh) signaling is implicated in the development of a variety of cancers. Smoothened (Smo) protein is a bottleneck in the Hh signal transduction. The regulation of the Hh signaling pathway to target the Smo receptor is a practical approach for development of anticancer agents. We report herein the design and synthesis of a series of 2-methoxybenzamide derivatives as Hh signaling pathway inhibitors. The pharmacological data demonstrated that compound 21 possessed potent Hh pathway inhibition with a nanomolar IC50 value, and it prevented Shh-induced Smo from entering the primary cilium. Furthermore, mutant Smo was effectively suppressed via compound 21. The in vitro antiproliferative activity of compound 21 against a drug-resistant cell line gave encouraging results.

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. However, fully utilizing the potential of Schottky barriers is still challenging. Herein, organic self-powered optoelectronic synapses with planar diode architecture are fabricated, which can simultaneously sense and process ultraviolet (UV) signals. The photovoltaic operations are facilitated by the built-in potential originating from the molecular-layer-defined asymmetric Schottky contacts. Diverse synaptic behaviors under UV light stimulation without external power supplies are facilitated by the interfacial carrier-capturing layer, which emulates the membranes of synapses. Furthermore, retina-inspired image preprocessing functions are demonstrated on the basis of synaptic plasticity. Therefore, our devices provide the potential for the development of power-efficient and advanced artificial visual systems.

The retina, the most crucial unit of the human visual perception system, combines sensing with wavelength selectivity and signal preprocessing. Incorporating energy conversion into these superior neurobiological features to generate core visual signals directly from incoming light under various conditions is essential for artificial optoelectronic synapses to emulate biological processing in the real retina. Herein, self-powered optoelectronic synapses that can selectively detect and preprocess the ultraviolet (UV) light are presented, which benefit from high-quality organic asymmetric heterojunctions with ultrathin molecular semiconducting crystalline films, intrinsic heterogeneous interfaces, and typical photovoltaic properties. These devices exhibit diverse synaptic behaviors, such as excitatory postsynaptic current, paired-pulse facilitation, and high-pass filtering characteristics, which successfully reproduce the unique connectivity among sensory neurons. These zero-power optical-sensing synaptic operations further facilitate a demonstration of image sharpening. Additionally, the charge transfer at the heterojunction interface can be modulated by tuning the gate voltage to achieve multispectral sensing ranging from the UV to near-infrared region. Therefore, this work sheds new light on more advanced retinomorphic visual systems in the post-Moore era.

Organic field-effect transistors (OFETs) have been the hotspot in information science for many years as the most fundamental building blocks for state-of-the-art organic electronics. During the field-effect modulation of the semiconducting channel, the gate dielectric always has a significant influence on the charge transport behaviours. Hence, understanding of the nature of charge carriers at the semiconductor/dielectric interface and realizing functional OFETs with superior performance have been the cornerstones for the sustainable advancement in organic electronics. With the joint efforts of predecessors, various basic theories and models have been advanced to describe the charge transport processes in organic crystals. To make a further breakthrough, more accurate correlation between the electrostatic properties of dielectrics and charge carrier behaviours is urgently needed. The high-quality interface-like films, without nonideal factors, two-dimensional molecular crystals (2DMCs), have been spotted as a powerful platform for direct and accurate characterization of the intrinsic charge transport behaviours at the semiconductor/dielectric interface. In this article, the recent breakthroughs in the physics of charge transport, interfacial effects, and perspectives with 2DMCs in OFETs are reviewed, providing great benefits to penetrate the fundamental studies and keep up with the revolutionary advancement in organic-electronics road map.