Low-Power Driving Waveform Design for Improving the Display Effect of Electrophoretic Electronic Paper.

To address the high power consumption associated with image refresh operations in EPDs, this paper proposes a low-power driving waveform that reduces the refresh power of EPDs by lowering the system's peak power. Compared to traditional waveforms, this waveform first activates the particles before erasing them, thus reducing voltage polarity changes. Additionally, it introduces a specific duration of 0 V voltage during the activation phase based on the physical characteristics of the electrophoretic particles to reduce the voltage span. Finally, a particular duration of 0 V voltage is introduced during the erasure phase to minimize the voltage span while ensuring the stability and consistency of the reference gray scale. The experimental results demonstrate that, in standard power tests, the new driving waveform reduces the power fluctuation value by 1.33% and the energy fluctuation value by 37.24% compared to the traditional driving waveform. This reduction in refresh power also mitigates screen flicker and ghosting phenomena.

Ultrasmall-sized light-emitting diodes fabricated by ion implantation based on GaN epitaxial wafers with fully activated or unactivated p-GaN.

A key challenge in realizing ultrahigh-resolution displays is the efficient preparation of ultrasmall-sized (USS) light-emitting diodes (LEDs). Today, GaN-based LEDs are mainly prepared through dry etching processes. However, it is difficult to achieve efficient and controllable etching of USS LED with high aspect ratios, and LED sidewalls will appear after etching, which will have a negative impact on the device itself. Herein, a method for preparing USS LED based on GaN epitaxial wafers is reported (on two types of wafers, i.e., with p-GaN fully activated and unactivated). F-ions are injected into the intentionally exposed areas on the two types of wafers to achieve device isolation. The area under the micro-/nano-sized protective masks (0.5, 0.8, 1, 3, 5, 7, 9, and 10 µm wide Ni/Au stripes) are the LED lighting areas. The LED on the p-GaN unactivated wafer (UAW) requires further activation. The Ni/Au mask not only serves as the p-electrode of LED but also Ni as a hydrogen (H) removing metal covering the surface of p-GaN UAW that can desorb H from a Mg element in the film at relatively low temperatures, thereby achieving the selective activation of LED lighting areas. Optoelectronic characterization shows that micro-/nano-sized LED arrays with individual-pixel control were successfully fabricated on the two types of wafers. It is expected that the demonstrated method will provide a new way toward realizing ultrahigh-resolution displays. Analyzing the changes in the current flowing through LED (before and after selective activation) on the F-injected p-GaN UAW, it is believed that depositing H removing metal on p-GaN UAW could possibly realize the device array through the selective activation only (i.e., without the need for ion implantation), offering a completely new insight.

Perovskite-Doped Modulated Color-Selective Photosynaptic Transistors for Target Object Recognition.

The processing of multicolor noisy images in visual neuromorphic devices requires selective absorption at specific wavelengths; however, it is difficult to achieve this because the spectral absorption range of the device is affected by the type of material. Surprisingly, the absorption range of perovskite materials can be adjusted by doping. Herein, a CdCl2 co-doped CsPbBr3 nanocrystal-based photosensitive synaptic transistor (PST) is reported. By decreasing the doping concentration, the response of the PST to short-wavelength light is gradually enhanced, and even weak light of 40 µW·cm-2 can be detected. Benefiting from the excellent color selectivity of the PST device, the device array is applied to feature extraction of target blue items and removal of red and green noise, which results in the recognition accuracy of 95% for the noisy MNIST data set. This work provides new ideas for the application of novel transistors integrating sensors and storage computing.

Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output.

Memristor-based physical reservoir computing holds significant potential for efficiently processing complex spatiotemporal data, which is crucial for advancing artificial intelligence. However, owing to the single physical node mapping characteristic of traditional memristor reservoir computing, it inevitably induces high repeatability of eigenvalues to a certain extent and significantly limits the efficiency and performance of memristor-based reservoir computing for complex tasks. Hence, this work firstly reports an artificial light-emitting synaptic (LES) device with dual photoelectric output for reservoir computing, and a reservoir system with mixed physical nodes is proposed. The system effectively transforms the input signal into two eigenvalue outputs using a mixed physical node reservoir comprising distinct physical quantities, namely optical output with nonlinear optical effects and electrical output with memory characteristics. Unlike previously reported memristor-based reservoir systems, which pursue rich reservoir states in one physical dimension, our mixed physical node reservoir system can obtain reservoir states in two physical dimensions with one input without increasing the number and types of devices. The recognition rate of the artificial light-emitting synaptic reservoir system can achieve 97.22% in MNIST recognition. Furthermore, the recognition task of multichannel images can be realized through the nonlinear mapping of the photoelectric dual reservoir, resulting in a recognition accuracy of 99.25%. The mixed physical node reservoir computing proposed in this work is promising for implementing the development of photoelectric mixed neural networks and material-algorithm collaborative design.

Toward Intelligent Display with Neuromorphic Technology.

In the era of the Internet and the Internet of Things, display technology has evolved significantly toward full-scene display and realistic display. Incorporating "intelligence" into displays is a crucial technical approach to meet the demands of this development. Traditional display technology relies on distributed hardware systems to achieve intelligent displays but encounters challenges stemming from the physical separation of sensing, processing, and light-emitting modules. The high energy consumption and data transformation delays limited the development of intelligence display, breaking the physical separation is crucial to overcoming the bottlenecks of intelligence display technology. Inspired by the biological neural system, neuromorphic technology with all-in-one features is widely employed across various fields. It proves effective in reducing system power consumption, facilitating frequent data transformation, and enabling cross-scene integration. Neuromorphic technology shows great potential to overcome display technology bottlenecks, realizing the full-scene display and realistic display with high efficiency and low power consumption. This review offers a comprehensive summary of recent advancements in the application of neuromorphic technology in displays, with a focus on interoperability. This work delves into its state-of-the-art designs and potential future developments aimed at revolutionizing display technology.

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.

Broadband beam collimation metasurface for full-color micro-LED displays.

Near-eye displays are widely recognized as a groundbreaking technological advancement with the potential to significantly impact daily life. Within the realm of near-eye displays, micro-LEDs have emerged as a highly promising technology owing to their exceptional optical performance, compact form factor, and low power consumption. However, a notable challenge in integrating micro-LEDs into near-eye displays is the efficient light collimation across a wide spectrum range. In this paper, we propose what we believe to be a novel design of a broadband beam collimation metasurface for full-color micro-LEDs by harnessing wavefront phase modulation based on Huygens' principle. Our results demonstrate a substantial reduction in the full width at half maximum (FWHM) angles, achieving a reduction to 1/10, 1/10, and 1/20 for red, green, and blue micro-LEDs compared to those without the metasurface, which is the best collimation result as far as we know. The central light intensity increases by 24.60, 36.49, and 42.15 times. Furthermore, the significant enhancement in the light energy within ±10° is achieved, with the respective multiplication factors of 14.16, 15.60, and 13.00. This metasurface has the potential to revolutionize the field by enabling high-performance, compact, and lightweight micro-LED displays, with applications in near-eye displays, micro-projectors, and beyond.

Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability.

Deep neural networks have revolutionized several domains, including autonomous driving, cancer detection, and drug design, and are the foundation for massive artificial intelligence models. However, hardware neural network reports still mainly focus on shallow networks (2 to 5 layers). Implementing deep neural networks in hardware is challenging due to the layer-by-layer structure, resulting in long training times, signal interference, and low accuracy due to gradient explosion/vanishing. Here, we utilize negative ultraviolet photoconductive light-emitting memristors with intrinsic parallelism and hardware-software co-design to achieve electrical information's optical cross-layer transmission. We propose a hybrid ultra-deep photoelectric neural network and an ultra-deep super-resolution reconstruction neural network using light-emitting memristors and cross-layer block, expanding the networks to 54 and 135 layers, respectively. Further, two networks enable transfer learning, approaching or surpassing software-designed networks in multi-dataset recognition and high-resolution restoration tasks. These proposed strategies show great potential for high-precision multifunctional hardware neural networks and edge artificial intelligence.

Effect of polymethyl methacrylate on in situ patterning of perovskite quantum dots by inkjet printing.

The preparation of perovskite quantum dots (PQDs) using an in situ inkjet printing method is beneficial for improving the problems of aggregation and photoluminescence (PL) quenching during long-term storage. However, the stability of PQDs prepared using this method is still not ideal, and the morphology of in situ-printed patterns needs to be optimized. To address these problems, this study introduced polymethyl methacrylate (PMMA) into the process of in situ inkjet printing of PQDs and explored the effect of PMMA on the in situ patterning effect of PQDs. The results showed that using a mixed precursor solution containing a small amount of PMMA as the printing ink can slow down the shrinkage process of ink droplets and improve the uniformity of film formation. As the printing substrate, PMMA provided a suitable high-viscosity environment for the in situ growth of PQDs. This could effectively suppress the coffee ring effect. In addition, the interaction between the C=O=C group in PMMA and metal ion Pb2+ in the CsPbBr3 precursor molecules was favourable to enhancing the density of PQDs. The prepared PMMA-coated CsPbBr3 quantum dots (QDs) pattern had high stability and could maintain at 90.08% PL intensity after 1 week of exposure to air.

Silver Nanowire Networks with Moisture-Enhanced Learning Ability.

The human brain possesses a remarkable ability to memorize information with the assistance of a specific external environment. Therefore, mimicking the human brain's environment-enhanced learning abilities in artificial electronic devices is essential but remains a considerable challenge. Here, a network of Ag nanowires with a moisture-enhanced learning ability, which can mimic long-term potentiation (LTP) synaptic plasticity at an ultralow operating voltage as low as 0.01 V, is presented. To realize a moisture-enhanced learning ability and to adjust the aggregations of Ag ions, we introduced a thin polyvinylpyrrolidone (PVP) coating layer with moisture-sensitive properties to the surfaces of the Ag nanowires of Ag ions. That Ag nanowire network was shown to exhibit, in response to the humidity of its operating environment, different learning speeds during the LTP process. In high-humidity environments, the synaptic plasticity was significantly strengthened with a higher learning speed compared with that in relatively low-humidity environments. Based on experimental and simulation results, we attribute this enhancement to the higher electric mobility of the Ag ions in the water-absorbed PVP layer. Finally, we demonstrated by simulation that the moisture-enhanced synaptic plasticity enabled the device to adjust connection weights and delivery modes based on various input patterns. The recognition rate of a handwritten data set reached 94.5% with fewer epochs in a high-humidity environment. This work shows the feasibility of building our electronic device to achieve artificial adaptive learning abilities.

Efficient CsPbBr3 Perovskite Light-Emitting Diodes via Novel Multi-Step Ligand Exchange Strategy Based on Zwitterionic Molecules.

Perovskite nanocrystals have absorbed increasing interest, especially in the field of optoelectronics, owing to their unique characteristics, including their tunable luminescence range, robust solution processability, facile synthesis, and so on. However, in practice, due to the inherent instability of the traditional long-chain insulating ligands surrounding perovskite quantum dots (PeQDs), the performance of the as-fabricated QLED is relatively disappointing. Herein, the zwitterion 3-(decyldimethylammonio)propanesulfonate (DLPS) with the capability of double passivating perovskite quantum dots could effectively replace the original long-chain ligand simply through a multistep post-treatment strategy to finally inhibit the formation of defects. It was indicated from theexperimental results that the DLPS, as one type of ligand with the bimolecular ion, was very adavntageous in replacing long-chain ligands and further suppressing the formation of defects. Finally, the perovskite quantum dots with greatly enhanced PLQY as high as 98% were effectively achieved. Additionally, the colloidal stability of the corresponding PeQDs has been significantly enhanced, and a transparent colloidal solution was obtained after 45 days under ambient conditions. Finally, the as-fabricated QLEDs based on the ligand-exchanged PeQDs exhibited a maximum brightness of 9464 cd/m2 and an EQE of 12.17%.

Boosting the Efficiency of High-Resolution Quantum Dot Light-Emitting Devices Based on Localized Surface Plasmon Resonance.

With pixel miniaturization, the performance of high-resolution quantum dot light-emitting diodes (QLEDs) usually degrades. Considering the dimension of ultrasmall pixels, herein, a barrier architecture based on localized surface plasmon resonance (LSPR) that promotes the radiative recombination of neighboring quantum dots is rationally designed to improve the device performance. Au nanoparticles (NPs) are embedded in an insulating polymer to form a honeycomb-patterned barrier layer via the nanoimprint process. Each pixel fabricated in the void area (average diameter of 1.5 µm) of the barrier layer is surrounded by a number of LSPR-NPs to enhance the luminescence. The resultant green QLEDs with a resolution of 9027 pixels per inch show a maximum external quantum efficiency of 11.1%, a 42.8% enhancement compared to the control device. Additionally, the lifetime of high-resolution QLEDs is obviously improved by the LSPR effect.

Toward grouped-reservoir computing: organic neuromorphic vertical transistor with distributed reservoir states for efficient recognition and prediction.

Reservoir computing has attracted considerable attention due to its low training cost. However, existing neuromorphic hardware, focusing mainly on shallow-reservoir computing, faces challenges in providing adequate spatial and temporal scales characteristic for effective computing. Here, we report an ultra-short channel organic neuromorphic vertical transistor with distributed reservoir states. The carrier dynamics used to map signals are enriched by coupled multivariate physics mechanisms, while the vertical architecture employed greatly increases the feedback intensity of the device. Consequently, the device as a reservoir, effectively mapping sequential signals into distributed reservoir state space with 1152 reservoir states, and the range ratio of temporal and spatial characteristics can simultaneously reach 2640 and 650, respectively. The grouped-reservoir computing based on the device can simultaneously adapt to different spatiotemporal task, achieving recognition accuracy over 94% and prediction correlation over 95%. This work proposes a new strategy for developing high-performance reservoir computing networks.

Ligand-Nondestructive Direct Photolithography Assisted by Semiconductor Polymer Cross-Linking for High-Resolution Quantum Dot Light-Emitting Diodes.

The photolithographic patterning of fine quantum dot (QD) films is of great significance for the construction of QD optoelectronic device arrays. However, the photolithography methods reported so far either introduce insulating photoresist or manipulate the surface ligands of QDs, each of which has negative effects on device performance. Here, we report a direct photolithography strategy without photoresist and without engineering the QD surface ligands. Through cross-linking of the surrounding semiconductor polymer, QDs are spatially confined to the network frame of the polymer to form high-quality patterns. More importantly, the wrapped polymer incidentally regulates the energy levels of the emitting layer, which is conducive to improving the hole injection capacity while weakening the electron injection level, to achieve balanced injection of carriers. The patterned QD light-emitting diodes (with a pixel size of 1.5 µm) achieve a high external quantum efficiency of 16.25% and a brightness of >1.4 × 105 cd/m2. This work paves the way for efficient high-resolution QD light-emitting devices.

Electron Oscillation-Induced Splitting Electroluminescence from Nano-LEDs for Device-Level Encryption.

Data security is a major concern in digital age, which generally relies on algorithm-based mathematical encryption. Recently, encryption techniques based on physical principles are emerging and being developed, leading to the new generation of encryption moving from mathematics to the intersection of mathematics and physics. Here, device-level encryption with ideal security is ingeniously achieved using modulation of the electron-hole radiative recombination in a GaN-light-emitting diode (LED). When a nano-LED is driven in the non-carrier injection mode, the oscillation of confined electrons can split what should be a single light pulse into multiple pulses. The morphology (amplitude, shape, and pulse number) of those history-dependent multiple pulses that act as carriers for transmitted digital information depends highly on the parameters of the driving signals, which makes those signals mathematically uncrackable and can increase the volume and security of transmitted information. Moreover, a hardware and software platform are designed to demonstrate the encrypted data transmission based on the device-level encryption method, enabling recognition of the entire ASCII code table. The device-level encryption based on splitting electroluminescence provides an encryption method during the conversion process of digital signals to optical signals and can improve the security of LED-based communication.

Uniformity improvement of a mini-LED backlight by a quantum-dot color conversion film with nonuniform thickness.

Mini-LED backlights energized by quantum-dot color conversion (QDCC) hold great potential for technological breakthroughs of liquid crystal displays. However, luminance uniformity issues should still be urgently solved owing to the large interval of direct-lit mini-LEDs, especially when covering with a QDCC film (QDCCF) with uniform thickness. Herein, we propose a uniformity improvement approach of mini-LED backlights by employing a QDCCF with nonuniform thickness based on the Lambertian distribution of mini-LEDs, which is demonstrated by screen-printing preparation and ray-tracing simulation. Experimental results show that the luminance uniformity of the nonuniform QDCCF can reach 89.91%, which is 24.92% higher than the uniform one. Ray-tracing simulation further elaborates the mechanism of this significant improvement. Finally, by employing this nonuniform QDCCF, a mini-LED backlight prototype is assembled and achieves high uniformity of 92.15%, good white balance with color coordinates of (0.3482, 0.3137), and high color gamut of 109% NTSC. This work should shed some new light on mini-LED-based display technology.

Research on Hydrodynamic Characteristics of Electronic Paper Pixels Based on Electrowetting.

In this paper, we propose a driving waveform with a complex ramp pulse for an electrowetting display system. The relationship between the contact angle and viscosity of inks was calculated based on the fluid-motion characteristics of different viscosities. We obtained the suitable range of viscosity and voltage in the liquid-oil-solid three-phase contact display system. We carried out model simulation and driving waveform design. The result shows that the driving waveform improves the response speed and aperture ratio of electrowetting. The aperture ratio of electrowetting pixels is increased to 68.69%. This research is of great significance to optimizing the structure of fluid material and the design of driving waveforms in electrowetting displays.

Metamaterials for light extraction and shaping of micro-scale light-emitting diodes: from the perspective of one-dimensional and two-dimensional photonic crystals.

Metamaterials have attracted broad attention owing to their unique versatile micro- and nano-structures. As a kind of typical metamaterial, photonic crystals (PhCs) are capable of controlling light propagation and constraining spatial light distribution from the chip level. However, introducing metamaterial into micro-scale light-emitting diodes (µLED) still exists many unknowns to explore. This paper, from the perspective of one-dimensional and two-dimensional PhCs, studies the influence of metamaterials on the light extraction and shaping of µLEDs. The µLEDs with six different kinds of PhCs and the sidewall treatment are analyzed based on finite difference time domain (FDTD) method, in which the optimal match between the PhCs type and the sidewall profile is recommended respectively. The simulation results show that the light extraction efficiency (LEE) of the µLEDs with 1D PhCs increases to 85.3% after optimizing the PhCs, and is further improved to reach 99.8% by the sidewall treatment, which is the highest design record so far. It is also found that the 2D air ring PhCs, as a kind of left-handed metamaterials, can highly concentrate the light distribution into 30° with the LEE of 65.4%, without help of any light shaping device. The surprising light extraction and shaping capability of metamaterials provides a new direction and strategy for the future design and application of µLED devices.

A sensory memory processing system with multi-wavelength synaptic-polychromatic light emission for multi-modal information recognition.

Realizing multi-modal information recognition tasks which can process external information efficiently and comprehensively is an urgent requirement in the field of artificial intelligence. However, it remains a challenge to achieve simple structure and high-performance multi-modal recognition demonstrations owing to the complex execution module and separation of memory processing based on the traditional complementary metal oxide semiconductor (CMOS) architecture. Here, we propose an efficient sensory memory processing system (SMPS), which can process sensory information and generate synapse-like and multi-wavelength light-emitting output, realizing diversified utilization of light in information processing and multi-modal information recognition. The SMPS exhibits strong robustness in information encoding/transmission and the capability of visible information display through the multi-level color responses, which can implement the multi-level pain warning process of organisms intuitively. Furthermore, different from the conventional multi-modal information processing system that requires independent and complex circuit modules, the proposed SMPS with unique optical multi-information parallel output can realize efficient multi-modal information recognition of dynamic step frequency and spatial positioning simultaneously with the accuracy of 99.5% and 98.2%, respectively. Therefore, the SMPS proposed in this work with simple component, flexible operation, strong robustness, and highly efficiency is promising for future sensory-neuromorphic photonic systems and interactive artificial intelligence.

Omnidirectional color shift suppression of full-color micro-LED displays with enhanced light extraction efficiency.

The three-primary-color chip array is the most straightforward to realize full-color micro-LED displays. However, the luminous intensity distribution shows high inconsistency between the AlInP-based red micro-LED and GaN-based blue / green micro-LEDs, resulting in the issue of angular color shift with different viewing angles. This Letter analyzes the angular dependence of color difference of conventional three-primary-color micro-LEDs, and proves that the inclined sidewall with homogeneous Ag coating has a limited angular regulation effect for micro-LEDs. Based on this, a patterned conical microstructure array is designed on the micro-LED's bottom layer to effectively eliminate the color shift. This design cannot only regulate the emission of full-color micro-LEDs to perfectly meet Lambert's cosine law without any external beam shaping elements, but also improve the light extraction efficiency of top emission by 16%, 161%, and 228% for red, green, and blue micro-LEDs, respectively. The color shift Δ u ' v ' of the full-color micro-LED display is also kept below 0.02 with the viewing angle ranging from 10° to 90°.