An All-in-One Bioinspired Neural Network

A Crayfish-Inspired Sensor Fusion Platform for Super Additive Integration of Visual, Chemical, and Tactile Information

The concept of cross-sensor modulation, wherein one sensor modality can influence another's response, is often overlooked in traditional sensor fusion architectures, leading to missed opportunities for enhancing data accuracy and robustness. In contrast, biological systems, such as aquatic animals like crayfish, demonstrate superior sensor fusion through multisensory integration. These organisms adeptly integrate visual, tactile, and chemical cues to perform tasks such as evading predators and locating prey. Drawing inspiration from this, we propose a neuromorphic platform that integrates graphene-based chemitransistors, monolayer molybdenum disulfide (MoS2) based photosensitive memtransistors, and triboelectric tactile sensors to achieve "Super-Additive" responses to weak chemical, visual, and tactile cues and demonstrate contextual response modulation, also referred to as the "Inverse Effectiveness Effect." We hold the view that integrating bio-inspired sensor fusion principles across various modalities holds promise for a wide range of applications.

Bionic Scotopic Adaptation Transistors for Nighttime Low Illumination Imaging

Human vision excels in perceiving nighttime low illumination due to biological feedforward adaptation. Replicating this ability in biomimetic vision using solid-state devices has been highly sought after. However, emulating scotopic adaptation, entailing a confluence of efficient photoexcitation and dynamic carrier modulation, presents formidable challenges. Here, we demonstrate a low-power and bionic scotopic adaptation transistor by coupling a light-absorption layer and an electron-trapping layer at the bottom of the semiconducting channel, enabling simultaneous achievement of efficient generation of free photocarriers and adaptive carrier accumulation within a single device. This innovation empowers our transistor to exhibit sensitivity-potentiated characteristics after adaptation, detecting scotopic-level illumination (0.001 lx) with exceptional photosensitivity up to 103 at low voltages below 2 V. Moreover, we have successfully replicated diverse scotopic vision functions, encompassing time-dependent visual threshold enhancement, light intensity-dependent adaptation index, imaging contrast enhancement for nighttime low illumination imaging, opening an opportunity for artificial night vision.

Memory-electroluminescence for multiple action-potentials combination in bio-inspired afferent nerves

The development of optoelectronics mimicking the functions of the biological nervous system is important to artificial intelligence. This work demonstrates an optoelectronic, artificial, afferent-nerve strategy based on memory-electroluminescence spikes, which can realize multiple action-potentials combination through a single optical channel. The memory-electroluminescence spikes have diverse morphologies due to their history-dependent characteristics and can be used to encode distributed sensor signals. As the key to successful functioning of the optoelectronic, artificial afferent nerve, a driving mode for light-emitting diodes, namely, the non-carrier injection mode, is proposed, allowing it to drive nanoscale light-emitting diodes to generate a memory-electroluminescence spikes that has multiple sub-peaks. Moreover, multiplexing of the spikes can be obtained by using optical signals with different wavelengths, allowing for a large signal bandwidth, and the multiple action-potentials transmission process in afferent nerves can be demonstrated. Finally, sensor-position recognition with the bio-inspired afferent nerve is developed and shown to have a high recognition accuracy of 98.88%. This work demonstrates a strategy for mimicking biological afferent nerves and offers insights into the construction of artificial perception systems.

A Butterfly-Inspired Multisensory Neuromorphic Platform for Integration of Visual and Chemical Cues

Unisensory cues are often insufficient for animals to effectively engage in foraging, mating, and predatory activities. In contrast, integration of cues collected from multiple sensory organs enhances the overall perceptual experience and thereby facilitates better decision-making. Despite the importance of multisensory integration in animals, the field of artificial intelligence (AI) and neuromorphic computing has primarily focused on processing unisensory information. This lack of emphasis on multisensory integration can be attributed to the absence of a miniaturized hardware platform capable of co-locating multiple sensing modalities and enabling in-sensor and near-sensor processing. In this study, this limitation is addressed by utilizing the chemo-sensing properties of graphene and the photo-sensing capability of monolayer molybdenum disulfide (MoS2 ) to create a multisensory platform for visuochemical integration. Additionally, the in-memory-compute capability of MoS2 memtransistors is leveraged to develop neural circuits that facilitate multisensory decision-making. The visuochemical integration platform is inspired by intricate courtship of Heliconius butterflies, where female species rely on the integration of visual cues (such as wing color) and chemical cues (such as pheromones) generated by the male butterflies for mate selection. The butterfly-inspired visuochemical integration platform has significant implications in both robotics and the advancement of neuromorphic computing, going beyond unisensory intelligence and information processing.

A bio-inspired visuotactile neuron for multisensory integration

Multisensory integration is a salient feature of the brain which enables better and faster responses in comparison to unisensory integration, especially when the unisensory cues are weak. Specialized neurons that receive convergent input from two or more sensory modalities are responsible for such multisensory integration. Solid-state devices that can emulate the response of these multisensory neurons can advance neuromorphic computing and bridge the gap between artificial and natural intelligence. Here, we introduce an artificial visuotactile neuron based on the integration of a photosensitive monolayer MoS2 memtransistor and a triboelectric tactile sensor which minutely captures the three essential features of multisensory integration, namely, super-additive response, inverse effectiveness effect, and temporal congruency. We have also realized a circuit which can encode visuotactile information into digital spiking events, with probability of spiking determined by the strength of the visual and tactile cues. We believe that our comprehensive demonstration of bio-inspired and multisensory visuotactile neuron and spike encoding circuitry will advance the field of neuromorphic computing, which has thus far primarily focused on unisensory intelligence and information processing.

Dielectrics for Two-Dimensional Transition-Metal Dichalcogenide Applications

Despite over a decade of intense research efforts, the full potential of two-dimensional transition-metal dichalcogenides continues to be limited by major challenges. The lack of compatible and scalable dielectric materials and integration techniques restrict device performances and their commercial applications. Conventional dielectric integration techniques for bulk semiconductors are difficult to adapt for atomically thin two-dimensional materials. This review provides a brief introduction into various common and emerging dielectric synthesis and integration techniques and discusses their applicability for 2D transition metal dichalcogenides. Dielectric integration for various applications is reviewed in subsequent sections including nanoelectronics, optoelectronics, flexible electronics, valleytronics, biosensing, quantum information processing, and quantum sensing. For each application, we introduce basic device working principles, discuss the specific dielectric requirements, review current progress, present key challenges, and offer insights into future prospects and opportunities.