123 results about "Optical neural network" patented technology

The invention discloses an end-to-end optical computing chip based on multi-mode analog signal fusion processing, and belongs to the optical computing technology. Comprising a multi-mode input fusion front-end module, an optical fiber input interface and an end-to-end reasoning module. The multi-mode input fusion front-end module can convert different types of original analog signals such as images, spectrums and radio frequencies into unified broadband spectrum input signals. The end-to-end reasoning module constructs a deep optical neural network, and the deep optical neural network comprises a sensing-convolution integrated unit realized by an arrayed waveguide grating (AWG). The end-to-end reasoning module further comprises a photoelectric non-linear-pooling integrated unit, the average pooling function and the non-linear activation function are achieved at the same time, and light path loss is effectively compensated through injection of the supply light source. And finally, integrating the signals subjected to multi-layer processing by a full connection layer, and outputting a classification result by an output layer. According to the chip architecture, direct and efficient processing of multi-mode analog signals is realized.

The invention provides a multiband photoresponse nerve synapse device based on a two-dimensional and organic heterostructure, an array and application, and belongs to the technical field of organic electric solid-state devices. The device provided by the invention adopts a three-end bottom gate top contact structure and comprises a gate substrate, an insulating layer, a channel layer and a source / drain electrode, and the channel layer forms an II-type heterojunction by a two-dimensional semiconductor material with a narrow-band gap characteristic and an organic semiconductor material. The heterostructure realizes ultraviolet to far infrared (365nm-10 [mu] m) multiband photoelectric response, and shows synaptic plasticity under light pulse modulation, including short-term enhancement, long-term enhancement and learning-forgetting-re-learning characteristics. Large-area array preparation of the device can be realized through methods such as chemical vapor deposition, solution spin coating, jet printing or layer-by-layer self-assembly. The technology not only can be used for brain-like learning and memory simulation, but also can be applied to the fields of sensing and memory integrated intelligent systems, optical neural networks, artificial vision, multi-mode intelligent sensing and the like.

The invention discloses an optical neural network non-linear activation system, method and device, and relates to the technical field of optical neural networks, because a magneto-optical material is integrated in a phase adjustment waveguide arm of a non-linear activation unit, a controller can adjust the current activation function type and weight of the optical neural network according to the current activation function type and weight of the optical neural network. And controlling a magneto-optical material of the phase adjustment waveguide arm to generate a magneto-optical effect so as to perform phase adjustment on an optical signal transmitted through the phase adjustment waveguide arm, so that the nonlinear activation unit can adapt to the type and weight of a current activation function, and perform corresponding nonlinear calculation on a target input signal to obtain a target input signal. Dynamic switching of various nonlinear activation functions is realized, and the flexibility of the optical neural network is improved.

The invention discloses a system and a method for realizing positive and negative weighting in an optical domain, and the system comprises a multi-wavelength light source array which provides multi-channel optical signals, and each wavelength corresponds to an input channel; the adjustable bias photoelectric modulator arrays are in one-to-one correspondence with the light sources, a quiescent working point is biased in a positive / negative slope linear region of a transmission curve through an independent bias circuit, positive and negative weight symbol control is realized, and an input electric signal is loaded; the multi-wavelength micro-ring weighting unit is used for weighting the modulated optical signal by regulating and controlling the transmissivity of the micro-ring; the photoelectric detection unit adopts a single-path detector to receive a composite optical signal and convert the composite optical signal into an electric signal; and the capacitor blocking and signal synthesis unit filters a direct current component, synthesizes an alternating current weighted signal and outputs a real number weighted sum. The system does not need to balance detectors and redundant devices, simplifies hardware architecture, reduces cost and size, reduces power consumption, improves weighting precision and reliability, supports flexible expansion of multi-wavelength channels, adapts to requirements of a large-scale optical neural network, and provides support for optical calculation integration and low-power-consumption landing.

An all-analog optical neural network includes multiple all-analog optical neural network layers; a laser and splitter configured to distribute light signals from the laser equally across all of the multiple all-analog optical neural network layers; integrated MZI switches configured to switch the all-analog optical neural network to a hybrid backpropagation training configuration that measures the light signals in forward and backward directions, and a trains a linear portion of the all-analog optical neural network. Preferably, each of the all-analog optical neural networks comprises: an integrated silicon photonic neural network (PNN) of Mach-Zehnder interferometers (MZIs) and programmable phase shifters (η) configured to implement a programmable unitary matrix-vector multiplication (MVM) operation U; photonic meshes configured to send input forward and backward inference signals to the PNN and configured to measure using both amplitude and phase detection an output forward signal and a backward adjoint signal from the PNN.

The invention provides an optical neural network computing system and method based on time domain excitation characteristics, and the system comprises a pulse clock sequence module which is used for generating a reference time sequence; the multi-channel pulse laser array is used for generating semiconductor neuron laser pulses with subnanosecond pulse width based on a reference time sequence; the time domain and wavelength division multiplexing module is used for simultaneously performing time domain coding and wavelength division multiplexing on the laser pulse based on a reference time sequence to generate a first optical signal; the reconfigurable weight matrix module is used for performing all-optical linear matrix operation and weight mapping on the first optical signal to obtain a second optical signal; the neuron activation module is used for performing nonlinear activation processing on the second optical signal based on an LIF theory to obtain an activated optical signal; and the output module is used for carrying out decoding and photoelectric conversion on the activated optical signal to obtain a network reasoning result.

The invention discloses an optical neural network multi-architecture adaptive computing chip and a computing method thereof. The chip comprises an optical signal input unit, an input data modulation unit, a weight and bias processing unit and a photoelectric detection unit. The optical signal input unit is used for generating and outputting an optical signal; the input data modulation unit is used for modulating and loading information carried by the source data and the bias data to an optical signal output by the optical signal input unit; the weight and bias processing unit comprises a photon weight module and a photon bias module, and the photon weight module is used for carrying out optical coding on weights and realizing optical multiplication and addition calculation of input data of optical signals and corresponding weight parameters; the photon bias module is used for bias coding, summing the bias coding and an optical multiplication and addition operation result, and outputting an operation result containing bias compensation; the photoelectric detection unit is used for converting operation results into electric signals to be output. The method has high universality, and the calculation accuracy of the optical neural network is effectively improved.

The exponential growth in deep learning models is challenging existing computing hardware. Optical neural networks (ONNs) accelerate machine learning tasks with potentially ultrahigh bandwidth and nearly no loss in data movement. Scaling up ONNs involves improving scalability, energy efficiency, compute density, and inline nonlinearity. However, realizing all these criteria remains an unsolved challenge. Here, we demonstrate a three-dimensional spatial time-multiplexed ONN architecture based on dense arrays of microscale vertical cavity surface emitting lasers (VCSELs). The VCSELs, coherently injection-locked to a leader laser, operate at gigahertz data rates with a 7T-phase-shift voltage on the 10-millivolt level. Optical nonlinearity is incorporated into the ONN with no added energy cost using coherent detection of optical interference between VCSELs.

The invention discloses a rapid biological cell classification imaging method and system based on a quantum correlation diffraction optical neural network, and the method comprises the steps: regulating and controlling a signal photon to an orbital angular momentum state optimized by a neural network based on a time-energy correlation two-photon pair generated in a spontaneous parametric down-conversion process, and irradiating a biological cell sample; a diffractive optical neural network is utilized to construct a quantum state illumination diffractive optical processor, photons carrying cell complex amplitude information are mapped to different detection areas of a predefined image plane, and rapid and high-precision cell classification and recognition are realized through correlation imaging results of an enhanced charge coupled camera. The method has the signal-to-noise ratio and anti-interference capability exceeding the classic limit, can keep high-precision cell classification capability under high background noise, and is suitable for long-time monitoring of living cells under the condition of extremely low light intensity. The method has the characteristics of low energy consumption and high efficiency, and meets the requirements of green calculation and sustainable development. And the method can be used for rapidly classifying and identifying the living biological cells without marks and phototoxicity.

The application discloses an optical neural network nonlinear activation system, method and device, relates to the technical field of optical neural networks, and because a magneto-optical material is integrated in a phase adjustment waveguide arm of a nonlinear activation unit, a controller can control the magneto-optical material of the phase adjustment waveguide arm to generate a magneto-optical effect according to a current activation function type and weights of the optical neural network, so as to perform phase adjustment on an optical signal transmitted through the phase adjustment waveguide arm, and make the nonlinear activation unit adapt to the current activation function type and the weights, perform corresponding nonlinear calculation on a target input signal, and realize dynamic switching of multiple nonlinear activation functions, thereby improving the flexibility of the optical neural network.

The application discloses a kind of diffractive light neural network computing systems and methods for realizing pure optical nonlinearity, belong to photonic computing and artificial intelligence hardware technical field.Its system includes coherent light source, linear light computing unit, nonlinear light activation unit and optical detection unit arranged in order along optical path, wherein linear light computing unit is programmable phase modulation using spatial light modulator, nonlinear light activation unit is programmable binary amplitude modulation using digital micromirror device, and both are directly coupled to form pure optical computing path without photoelectric conversion on optical path.Its method trains neural network through customized loss function, drives nonlinear activation function output to binary convergence, and maps the parameters obtained by training into phase map and binary mask respectively and loads to hardware.The application realizes true all-optical nonlinear computation, with the advantages of high energy efficiency, extremely low delay, compact structure and strong parallel processing capability.

The application discloses a design method and device of a metasurface optical neural network, and the design method comprises the following steps: obtaining an artificial neural network model and a metasurface optical neural network model, wherein the artificial neural network model is a trained model; selecting n intermediate layers in the artificial neural network model in the order from shallow to deep, one-to-one corresponding the n intermediate layers and n metasurface layers in sequence, and calculating an intermediate feature loss according to the similarity between the features output by each layer in the n intermediate layers and the features output by the corresponding metasurface layer; training the metasurface optical neural network model; and determining the process parameters of the metasurface according to the trained metasurface optical neural network model. The design method completely transplants the calculation capacity of the artificial neural network into the metasurface optical neural network, solves the problems of low design efficiency and low precision of the metasurface optical neural network, and is beneficial to the development of the metasurface optical neural network and the deployment in an environment with limited computing power and storage.

The invention discloses an optical nonlinear activator chip based on a graphene photoelectric device and a preparation method. Belongs to the technical field of photoelectric devices and optical signal processing. The chip comprises a detector optical path and a modulator optical path, a substrate is made of a standard SOI material, and the chip structurally comprises an optical coupling module, a waveguide, a graphene detector part and a graphene modulator part. The graphene detector and the modulator are integrated on the same chip, the detector is used for converting an input optical signal into a voltage signal, then the voltage signal is input into the modulator to modulate continuous light, and an optical signal after nonlinear conversion is output and used for a nonlinear activation unit of an optical neural network. Compared with a traditional nonlinear activator of a silicon-based integrated photon chip, the method gives full play to the advantages of excellent photoelectric property and easy integration of graphene, has the advantages of high integration level, high speed, low power consumption and the like, and has better compatibility with a silicon-based optoelectronic process.

The application provides a matrix operation accelerator combining wavelength division multiplexing and MZI cascade network, relates to the field of optical neural networks, and comprises an input signal layer, a weight signal layer, a summation layer and a nonlinear layer.The input signal layer is used for realizing matrix operation of optical signals through a Mach-Zehnder interferometer array; the weight signal layer is used for applying an electrical signal to a micro-ring modulator array to adjust a weight signal; the summation layer is used for separating the results of the action of the optical signals of different wavelengths through the weight signal; and the nonlinear layer is used for converting the optical signals into electrical signals through a photodetector array to realize a nonlinear activation function in the electrical domain.The application introduces N different wavelengths in the network formed by MZI cascade, so that the number of times of executing matrix operation is increased by N times each time, high-speed convolution operation is facilitated, and the size of the micro-ring modulator is relatively small, so that the energy efficiency and area ratio of the MZI cascade network calculation can be effectively increased.

The invention discloses a method for optically realizing a restricted Boltzmann machine. According to the invention, analog calculation of the restricted Boltzmann machine and optical Gibbs sampling can be realized, and the system has the advantages of wide application range, fast information transmission, simple structure, low cost, fast calculation and the like. According to the invention, a coherent wide-spectrum light source is used as signal input, a light field is subjected to light splitting by using a light splitting device such as a grating and then enters a modulator, then spinning, interaction and magnetic field parameters are coded on light wavefront at different positions through a time or space light modulator, and optical Fourier transform is carried out by using a time or space lens system, so that the optical field is obtained. The light intensity after Fourier transform is measured through the detector, and finally the difference between the light intensity measured twice is calculated, so that optical Gibbs sampling is realized, the calculation complexity is reduced, and the calculation efficiency is improved. The method provided by the invention has important application prospects in the fields of optical neural networks and the like, can realize applications of content generation, classification and identification and the like, and is convenient to integrate in optical chips and the like.

The application aims to provide a kind of micro-ring resonator-based activation function device manufacturing method, comprising the following steps: determining the standard nonlinear function to be fitted by optical device;Preparation of micro-ring resonator;Coupling ring PN junction and micro-ring;Set up auxiliary light source and output signal measurement equipment;Set up TIA transimpedance amplifier and bias unit.The application can realize the function of activation function by optical device, and it is relatively easy to combine with other optical elements that can realize the summation of neural unit weight, realize the complete function of single neuron, thereby improve the operation efficiency and operation accuracy of optical neural network.The device is small in size and easy to integrate, and the operation efficiency and operation accuracy of the optical neural network integrated by the neural unit integrated by the device can be significantly improved.

A method for identifying an unknown target in a complex environment by using an all-optical diffraction neural network is characterized in that the adopted all-optical diffraction neural network is composed of multiple layers of phase-type diffraction optical elements, multiple wavefront regulation and control can be performed on a light field corresponding to an input image, and classification and identification of the image are completed in an optical domain after diffraction propagation of a certain distance; on the basis of a diffraction neural network, an anti-interference recognition mechanism is provided, concepts of a target object and an interferent which need to be specifically classified are provided in a diffraction neural network training stage, and a loss function and a constraint condition are set respectively; in combination with a wavelength multiplexing mechanism, different target object classification tasks are independently processed under different wavelength channels, so that classification results of all wavelength channels are integrated in a full-wave band range, the classification task of unknown targets in a complex environment is realized, and the advantages of high speed and low energy consumption of the all-optical diffraction neural network are exerted. The limitations of simple application scene, small target number and the like of the optical neural network are overcome.

The invention is suitable for the technical field of optical computing, and provides a knowledge distillation-based on-chip optical neural network design method, which comprises the following steps of: constructing an initial population of an optimization algorithm based on a preset process constraint condition; the plurality of individuals in the initial population are in one-to-one correspondence with the plurality of groups of optical neural network parameters; performing iterative optimization on the initial population by using an optimization algorithm to obtain an optimal optical neural network parameter meeting a preset process constraint condition; constructing an optical neural network based on the optimal optical neural network parameters, and taking the constructed optical neural network as an on-chip optical neural network; the calculation process of the fitness value of the individual in the optimization algorithm comprises the following steps: training the optical neural network corresponding to the individual by using the knowledge distillation framework, and determining the fitness value based on the training loss value and the physical parameter of the optical neural network. According to the invention, the area of the on-chip optical neural network is reduced, the complexity of the on-chip optical neural network is reduced, and the performance of the on-chip optical neural network in processing complex tasks is improved.

The invention discloses a block type optical neural network, a matrix calculation method, a chip and electronic equipment, and the block type optical neural network comprises at least two input modules which are arranged side by side at intervals; the output modules are arranged side by side at intervals; the at least two sub-processing modules are arranged in an array, the sub-processing modules located in the same row are in coupled connection with the same input module, and the sub-processing modules located in the same column are in coupled connection with the same output module; wherein the sub-processing modules coupled and connected with the same input module have the same input optical signal, and each output module is configured to superpose the optical field output by each sub-processing module coupled and connected with the output module. Compared with an optical neural network in the prior art, when the partitioned optical neural network provided by the invention processes a calculation task, each sub-processing module performs parallel processing, so that the error accumulation is remarkably reduced, and the calculation precision can be improved.

The invention discloses an optical switch device and a complex-valued optical neural network system, and belongs to the technical field of optical computing. The optical switch device is a two-input two-output device and comprises first to sixth waveguides, and first and second multimode interference couplers. Phase shifters are attached to the third waveguide and the fifth waveguide and / or the fourth waveguide and the sixth waveguide to form a group of configuration units which are used for loading real part and imaginary part data of a complex number. The phase shifter comprises a nonvolatile phase change material layer and a heating layer which are sequentially arranged from bottom to top, is used for realizing the function of the phase shifter, greatly reduces the static power consumption, improves the energy efficiency, is suitable for realizing the complex value optical neural network, and is also beneficial to large-scale integration. The complex-valued optical neural network system constructed based on the optical switch integrates input, calculation, reference and coherent detection modules, can synchronously complete amplitude and phase detection of optical signals, effectively supports complex-valued optical calculation, and has the advantages of low power consumption and high integration level.

The application discloses an optical matrix vector multiplier based on a unitary matrix-diagonal matrix decomposition. The multiplier comprises a Mach-Zehnder interferometer array for realizing a synthetic unitary matrix Omega and a modulation unit for realizing a corresponding diagonal matrix Sigma, wherein a target weight matrix M satisfies a decomposition form of M=ΩΣ. The method fuses two unitary matrices required for mapping in a traditional singular value decomposition into a single synthetic unitary matrix Omega, so that a unitary transformation can be completed by only one interferometer array of optical hardware, and a complete matrix multiplication is realized in cooperation with the modulation unit. Through algorithm-hardware collaborative design, the number of basic optical interference units required is significantly reduced, the hardware complexity, manufacturing cost and system power consumption of the optical neural network are greatly reduced while the computing performance is maintained, and an effective solution is provided for efficient optical computing acceleration of artificial intelligence.

The application discloses an end-to-end optical computing chip based on multi-modal analog signal fusion processing, and belongs to the technical field of optical computing. The chip comprises a multi-modal input fusion front-end module, a fiber input interface and an end-to-end inference module. The multi-modal input fusion front-end module can convert different types of original analog signals such as images, spectra and radio frequencies into unified broadband spectral input signals. The end-to-end inference module builds a deep optical neural network, which comprises a "sensing-convolution integrated" unit realized by an arrayed waveguide grating (AWG). The end-to-end inference module also comprises an optoelectronic nonlinear-pooling integrated unit, which simultaneously realizes the functions of average pooling and nonlinear activation, and effectively compensates for optical path loss through injection of a light source. Finally, the signals processed by multiple layers are integrated by a full connection layer, and a classification result is output by an output layer. The chip architecture realizes direct and efficient processing of multi-modal analog signals.

The invention relates to the technical field of spectral imaging, and discloses a multichannel scattered light network construction method and system based on a photon lead, and the method comprises the steps: obtaining scattered light spots of a target object through an optical imaging module, and then carrying out the multiple scattering of the scattered light spots through at least three layers of reflection media, the method comprises the following steps: performing forward calculation and information dimension reduction on an optical neural network, coupling a scattering output signal to a single optical fiber, inputting the scattering output signal into a spectrograph to obtain spectral data, and training an initial optical analysis network by taking a target object as input and the spectral data as output to establish a target-spectrum mapping model so as to obtain a trained optical analysis network. Accurate spectral data can be obtained according to the optical system, model training is performed according to the spectral data, the optical analysis network is obtained, and the spectral data type corresponding to the target object is efficiently analyzed.

The application discloses a three-dimensional heterogeneous integrated optical computing chip, and belongs to the optical computing field.The computing chip is composed of three parts: a spatial diffraction optical neural network core particle, a planar matrix reconfigurable optical neural network component and a planar switchable optical switch module; a single or multiple spatial diffraction optical neural network core particles, planar matrix reconfigurable optical neural network components and planar switchable optical switch modules are combined together in a three-dimensional heterogeneous integrated manner to realize efficient multi-task optical computing processing.The application utilizes the mutual combination of the spatial diffraction optical neural network and the planar matrix reconfigurable optical neural network, breaks through the traditional optical computing architecture, has a wide application prospect in the optical computing field, and fills the blank in the related technical field.

This invention discloses a highly self-assembled dual quantum dot / graphene heterojunction broadband memristor, its fabrication method, and its applications, belonging to the field of inorganic semiconductor technology. The method includes the following steps: mixing highly self-assembled cerium oxide quantum dot ink and PbS quantum dot ink with a graphene dispersion and stirring until homogeneous to form a uniformly loaded quantum dot / graphene dispersion; then using the quantum dot / graphene dispersion to construct a dual quantum dot / graphene heterojunction film on a silicon wafer surface, thus obtaining the dual quantum dot / graphene heterojunction broadband memristor. This invention overcomes the inherent limitations of the photoresponse range of single materials, achieving broadband optoelectronic memristor behavior in the ultraviolet to near-infrared bands. Simultaneously, the memristor of this invention exhibits rich synaptic plasticity under light pulse modulation, providing core support for constructing a novel integrated optoelectronic system for broadband sensing, storage, and processing. It possesses strong technical adaptability and industrialization prospects in cutting-edge fields such as bionic vision, optical neural networks, and intelligent sensing.

A machine vision task, machine learning task, and / or classification of objects is performed using a diffractive optical neural network device. Light from objects passes through or reflects off the diffractive optical neural network device formed by multiple substrate layers. The diffractive optical neural network device defines a trained function between an input optical signal from the object light illuminated at a plurality or a continuum of wavelengths and an output optical signal corresponding to one or more unique wavelengths or sets of wavelengths assigned to represent distinct data classes or object types / classes created by optical diffraction and / or reflection through / off the substrate layers. Output light is captured with detector(s) that generate a signal or data that comprise the one or more unique wavelengths or sets of wavelengths assigned to represent distinct data classes or object types or object classes which are used to perform the task or classification.

A photonic gate system comprising a center waveguide that is provided with a continuous wave input; a first electrically controlled plasmonic waveguide configured on a first opposing side that is adjacent to the center waveguide; a second electrically controlled plasmonic waveguide configured on a second opposing side that is adjacent to the center waveguide; a first outer waveguide configured adjacent to the first electrically controlled plasmonic waveguide; and a second outer waveguide configured adjacent to the second electrically controlled plasmonic waveguide.

The invention discloses an imaging method for passing through a random scattering medium in visible light based on a diffractive optical neural network, and the method comprises the following steps: 1, carrying out the optical coding of an input image through a coherent or low-coherent monochromatic plane wave, and enabling a coded light field to pass through a random phase diffuser for scattering; and step 2, continuous wavefront modulation of the scattered light field is obtained through a coherent or incoherent diffractive optical neural network, and finally, high-fidelity original image reconstruction is generated on an output plane. The invention finds that the coherent diffraction neural network obtains dynamic phase modulation by introducing a randomly generated diffuser in the training process, enhances the adaptive capacity of the network to the spatial coherence change of the light source, realizes the high-quality reconstruction of the scattering image under the visible light, has the robustness to the low-coherence light source, and is suitable for large-scale popularization and application. The method can be applied to low-power-consumption and real-time imaging of the dynamic scattering medium under natural light.

The invention relates to an optical neural network topology adaptive mode division multiplexing communication system and a training method, and the method provided by the invention is applied to a mode division multiplexing communication system, and is used for solving the problem that the transmission or calculation performance is reduced due to dynamic coupling crosstalk of a spatial mode caused by environmental disturbance. The method comprises the following steps: monitoring the output of an optical neural network in real time, and generating a state matrix representing mode crosstalk; extracting matrix features to construct an environment vector; through a pre-trained deep reinforcement learning network, a reconstruction action for controlling the adjustable photonic device is decided and generated according to the vector; the driving device dynamically adjusts the network physical topology to compensate crosstalk; and finally, optimizing the strategy network on line based on the reconstructed performance evaluation result to form a closed loop. According to the method, the optical neural network in the mode division multiplexing has online self-adaptive capability, the dynamic mode crosstalk can be continuously inhibited, and the stability and high performance of the system in actual deployment are guaranteed.

The invention discloses an anti-laser interference imaging system based on intelligent optical calculation, and belongs to the technical field of optics. According to the invention, a set of end-to-end laser immune imaging system from a physical layer to an algorithm layer is constructed, a trainable physical optical neural network is integrated in an imaging light path as a hardware front end, and physical inhibition of laser interference is realized in an optical signal stage through a wavefront modulation mechanism. By utilizing the essential difference between the coherence of the laser and the incoherence of the natural light in physical characteristics, energy scattering is performed on the laser with a specific wavelength through an optimally designed phase modulation diagram, and meanwhile, high-flux transmission of the natural light is kept, so that an overexposure phenomenon is effectively avoided before the sensor receives the light. The mechanism not only solves the problems that a traditional optical filter is fixed in bandwidth and poor in adaptability, but also breaks through the bottleneck that details are difficult to recover due to information loss in pure algorithm processing. Meanwhile, the anti-interference of the multi-spectrum laser is innovatively designed, so that the system can realize wider-spectrum laser protection.