The data increase necessitates high-capacity, low-energy optical data storage. Bridging the memory gap between units and processors requires techniques and materials mimicking the efficiency of the human brain’s memory based on synaptic plasticity. Optical techniques show promise, yet energy-efficient optical data storage compels advanced media. Upconversion nanoparticles are luminescent nanomaterials for high-capacity, low-energy optical data storage. We used upconversion nanoparticles for ultra-low-energy optical data storage with synaptic-like behaviour by switching upconversion states under low light power irradiation. We achieved features with sub-nanojoule-level energy consumption and synaptic-like functions of the human brain’s memory, enabling short-term and long-term memory.
We develop a novel Fourier-domain optical convolutional neural networks (FOCNNs) with multi-stage framework to hierarchical learn the image features at the speed of light. The FOCNN consists of two optical convolutional layers integrated with multiple parallel kernels and one optical fully-connected layer to form an all-optical CNN-like physical network structure. The FOCNN convolute the whole Fourier spectrum of the objects rather than the local receptive field of the objects, so it could extract the global and non-local features of the objects. In addition, the vortex phase is introduced to the optical convolutional kernels to extract the edge features. We incorporate this Fourier optics-based, parallel, one-step FOCNN in the tasks of semantic segmentation for pixel-level classification, and the capability of video-rate segmentation for objects is also demonstrated based on the programmable spatial light modulators, which demonstrated the computational power of FOCNN located in the range of Peta operations per second (POPS). Therefore, the FOCNN is useful for the real-time dynamic inference tasks, such as robotic vision, autonomous driving, and so on.
Far-field super-resolution optical technology provides ways for high-capacity super-resolution optical data storage. Typical techniques necessitate high laser beam power and lead to photo-damage. Since they can convert near-infrared excitation to ultraviolet and visible emission, upconversion nanoparticles have potential for photo-activation. Furthermore, they have excited energy levels with long lifetime for low-power super-resolution optical microscopy. We demonstrate the application of upconversion nanoparticles with high-order luminescence emission for low-power super-resolution photo-activation for low-power super-resolution optical data storage. Upconversion nanoparticles were mixed with photo-active compounds. To stimulate photo-activation in the nanocomposite, super-resolution irradiation was used. Written features demonstrated super-resolution size upon low laser beam power.
Photopolymerization induced by up conversion nanoparticles (UCNPs) are reported to have promising potential in the biological and nano-imaging field. Here, a novel method of nanoscale writing at low power level is demonstrated through the incorporation of UCNPs under a two-beam far-field direct laser writing (DLW) configuration. Equipped with long lifetime of excited energy levels, UCNPs were employed to function as the excitation light source for inducing controlled reversible deactivation radical polymerization through activating polymerization photo reagents via resonance energy transfer in the localized area surrounding the UCNPs, hence generating polymerized micro-scale features upon an incident near-infrared laser beam.
UCNPs with unique emission qualities were custom-synthesized and dispersed in a monomer-based mixture containing polymerization photo-reagents to formulate a photo-sensitive nanocomposite. A thin film sample based off the nanocomposite was then placed under a two-beam super-resolution writing scheme for the fabrication of 3D micro-structures at low power level (100sW/cm2 for the writing laser beam intensity).
Able to generate 3D nanoscale-features at low power level with unique photo-luminescent properties in comparison with the traditional two-photon writing, this new nanoscale writing technique possesses significant application potential in fields of nanophotonics such as 3D micro-prototyping, 3D low-power nanoscale optical data storage, nanoscale-resolution imaging and functional nanoscale-photonic devices.
Far-field super-resolution optical technologies offer methods to high-capacity nanoscale optical memory. Typical approaches need high beam power and energy consumption. Because they can convert near-infrared excitation to ultraviolet and visible emission, upconversion nanoparticles show potential for photo-activation. Upconversion nanoparticles have metastable excited energy levels, enabling low-power stimulated emission depletion microscopy. We show the use of upconversion nanoparticles for low-power nanoscale photo-activation for high-capacity low-energy consumption optical memory.
Upconversion nanoparticles were combined with photo-active compounds. Super-resolution irradiation excited upconversion nanoparticles for photo-activation in the nanocomposite. Written features showed nanoscale size under low-intensity irradiation and enabled multiple optical readouts.
The zero index metamaterials (ZIM) have been an intense research topic in nanophotonics. In ZIMs, the effective wavelength becomes infinite, and the spatial phase distribution of the propagating wave becomes uniform in the medium, overcoming many limitations imposed by the short spatial wavelength in the optical regime. This feature of ZIMs leads to applications including on-chip super coupling, nonlinear pumping, and on-chip orbital angular momentum generation. However, the traditional methods to realize ZIM face Ohm loss or out-of-plane radiation. In this work, we propose Steiner tree networks featuring a Dirac-like point and a photonic stop gap to realize low-loss 3D ZIM.
Optical encryption plays an important role in information encryption. As an ultrathin optical elements, metasurfaces have the ability to precisely and fully control the incident light, which greatly promotes the development of optical information encryption. Here, we propose and demonstrate a method for optical encryption based on different spin-states of incident light via different intensity distributions generated by metasurface. The meta-devices with a numerical aperture (NA) of 0.6 and a diameter of 27 microns can independently encode two different binary numbers, where the light field distributions of the donut-shaped beam and the solid-shaped beam represent 0 and 1, respectively. On this basis, the optical encryption of information can be achieved through American Standard Code for Information Interchange (ASCII). The proposed method provides an ultracompact and highly secure platform for large-scale information encoding.
The rapid development of artificial intelligence has stimulated the interest of the novel designs of photonic neural networks (PNNs) because of the high speed, low energy consumption and parallelism nature of the light. Based on optical holographic technology, a kind of three-dimensional PNNs, diffractive neural networks (DNNs), have demonstrated their superb performance in parallel two-dimensional data processing. DNNs are composed of multi-layer cascaded holographic plates. Relying on the diffraction of the incident light, each pixel in every layer can be connected with multiple pixels in the next layer to mimic the architecture of the biological nervous system. Important applications, such as image recognition, optical logic operation, and image reconstruction, have been realized on DNNs with high operation efficiency. However, in most of the reported works, the layers of DNNs are spatially separated with a large size of centimeter-scale, which greatly limits the on-chip integration of DNNs. In this work, we reported a green-light bilayer integrated DNNs. The two layers of the DNNs were integrated on the double sides of a quartz wafer respectively by lithography followed by dry etching. Based on the theory of diffraction, the DNNs were trained with a size of millimeter-scale. When the DNNs work, the incident optical signal first passes through the 1st layer of the DNNs, then diffracts inside the quartz wafer, and finally emitted out from the 2nd layer of the DNNs on the backside. Handwritten digital recognition of 0~1 (89 % accuracy) or 0~9 (65% accuracy) was successfully realized. The high stability of quartz provides the basis for the long-term reliable operation of DNNs. The manufacturing of the DNNs is compatible with the mature semiconductor manufacturing technology, which provides a feasible route for the macro fabrication of DNNs.
Photopolymerization assisted by up conversion nanoparticles (UCNPs) are reported to have promising potential in the biological field due to the unique fluorescent features of UCNPs. Here, we demonstrate a novel method in the fabrication of three-dimensional (3D) features at low power level with unique photo-luminescence property through the incorporation of UCNPs under a far-field direct laser writing (DLW) configuration. Equipped with long lifetime of excited energy levels, UCNPs were employed to function as the excitation light source for inducing controlled reversible deactivation radical polymerization through activating polymerization photo reagents via resonance energy transfer in the localized area surrounding the UCNPs, hence generating polymerized micro-scale features upon an incident near-infrared laser beam. UCNPs with unique emission qualities were custom-synthesized and dispersed in a monomer-based mixture containing polymerization photo-reagents to formulate a photo-sensitive nanocomposite. A thin film sample based off the nanocomposite was then placed under a DLW scheme for the fabrication of 3D micro-structures at low power level (100sW/cm2 for the writing laser beam intensity). Able to fabricate 3D micro-structures at very low power level with unique photo-luminescent properties compared to the traditional two-photon polymerization technique, this new method of laser fabrication method assisted by UCNPs has significant potential applications in research domains such as 3D low-power nanoscale optical memory, high-resolution imaging/display, functional micro-photonics devices and 3D micro-prototyping.
The creation of biomimetic neuron interfaces (BNIs) has become imperative for different research fields from neural science to artificial intelligence. BNIs are two-dimensional or three-dimensional (3D) artificial interfaces mimicking the geometrical and functional characteristics of biological neural networks to rebuild, understand, and improve neuronal functions. The study of BNI holds the key for curing neuron disorder diseases and creating innovative artificial neural networks (ANNs). To achieve these goals, 3D direct laser writing (DLW) has proven to be a powerful method for BNI with complex geometries. However, the need for scaled-up, high speed fabrication of BNI demands the integration of DLW techniques with ANNs. ANNs, computing algorithms inspired by biological neurons, have shown their unprecedented ability to improve efficiency in data processing. The integration of ANNs and DLW techniques promises an innovative pathway for efficient fabrication of large-scale BNI and can also inspire the design and optimization of novel BNI for ANNs. This perspective reviews advances in DLW of BNI and discusses the role of ANNs in the design and fabrication of BNI.
Nanoscale optical writing enables high-density optical data storage. However, current techniques usually require high laser beam intensity with high energy consumption and short device lifetime. Upconversion nanoparticles (UCNPs) have shown great potential for high-density optical data storage due to their exceptional luminescence emissions. In addition, UCNPs have enabled low-power STED microscopy. We show that UCNPs can induce the reduction of graphene oxide (GO) at the nanoscale. Dual-beam super-resolution irradiation was used to write features in UCNP-conjugated GO with lateral feature size at the nanoscale and inhibition intensity of <15 MW/cm^2. This approach might offers a convenient and energy-efficient solution for the storage demands in the Data Age.
The highly efficient modulation of the luminescence from upconversion nanoparticles combined with graphene oxide in a thin film was achieved at a millisecond timescale through the photochemical reduction of graphene oxide under UV irradiation. The experimental design comprised the integration of the upconversion nanoparticles with graphene oxide to form a reproducible and scalable thin film. This design enabled convenient testing of the sample with a home‐built optical system setup comprising an UV CW laser at 375 nm for the photochemical reduction of graphene oxide and a near-infrared CW laser at 980 nm for the excitation of the upconversion nanoparticles. The recovery of the graphene‐like structure through the photochemical reduction of graphene oxide was accompanied by a variation in the absorption coefficient of the thin film, which enabled super‐quenching of the luminescence from the upconversion nanoparticles under near‐infrared excitation with values of up to ~90%. Further, the instantaneous reduction in the intensity upon UV irradiation offered decreased modulation time of upconversion luminescence down to milliseconds at microwatt‐level power. Optical patterning was successfully produced in the thin film: representations of a leaf, the Sydney Opera House and a kangaroo were fabricated in the thin film and recovered by raster scanning the sample. The resulting patterns had high spatial resolution for upconversion luminescence modulation down to the diffraction limit for the considered wavelengths. These findings pave the way toward prompt use of this novel thin film for display technologies, photoswitching in optoelectronic devices, and optical data storage applications.
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