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Phase imaging is widely used in biomedical imaging, sensing, and material characterization, among other fields. However, direct imaging of phase objects with subwavelength resolution remains a challenge. Here, we demonstrate subwavelength imaging of phase and amplitude objects based on all-optical diffractive encoding and decoding. To resolve subwavelength features of an object, the diffractive imager uses a thin, high-index solid-immersion layer to transmit high-frequency information of the object to a spatially-optimized diffractive encoder, which converts/encodes high-frequency information of the input into low-frequency spatial modes for transmission through air. The subsequent diffractive decoder layers (in air) are jointly designed with the encoder using deep-learning-based optimization, and communicate with the encoder layer to create magnified images of input objects at its output, revealing subwavelength features that would otherwise be washed away due to diffraction limit. We demonstrate that this all-optical collaboration between a diffractive solid-immersion encoder and the following decoder layers in air can resolve subwavelength phase and amplitude features of input objects in a highly compact design. To experimentally demonstrate its proof-of-concept, we used terahertz radiation and developed a fabrication method for creating monolithic multi-layer diffractive processors. Through these monolithically fabricated diffractive encoder-decoder pairs, we demonstrated phase-to-intensity (P → I) transformations and all-optically reconstructed subwavelength phase features of input objects (with linewidths of ~ λ/3.4, where λ is the illumination wavelength) by directly transforming them into magnified intensity features at the output. This solid-immersion-based diffractive imager, with its compact and cost-effective design, can find wide-ranging applications in bioimaging, endoscopy, sensing and materials characterization. [ABSTRACT FROM AUTHOR]

Optical phase conjugation (OPC) is a nonlinear technique used for counteracting wavefront distortions, with applications ranging from imaging to beam focusing. Here, we present a diffractive wavefront processor to approximate all-optical phase conjugation. Leveraging deep learning, a set of diffractive layers was optimized to all-optically process an arbitrary phase-aberrated input field, producing an output field with a phase distribution that is the conjugate of the input wave. We experimentally validated this wavefront processor by 3D-fabricating diffractive layers and performing OPC on phase distortions never seen during training. Employing terahertz radiation, our diffractive processor successfully performed OPC through a shallow volume that axially spans tens of wavelengths. We also created a diffractive phase-conjugate mirror by combining deep learning-optimized diffractive layers with a standard mirror. Given its compact, passive and multi-wavelength nature, this diffractive wavefront processor can be used for various applications, e.g., turbidity suppression and aberration correction across different spectral bands. The authors present a diffractive optical processor that approximates optical phase conjugation operation without any digital computing. This compact and all-optical wavefront processor can be used for various applications, including turbidity suppression and aberration correction. [ABSTRACT FROM AUTHOR]

Imaging systems operating in the terahertz part of the electromagnetic spectrum are attractive due to their ability to penetrate many opaque materials and provide unique spectral signatures of various chemicals. However, the use of terahertz imagers in real-world applications has been limited by the slow speed, large size, high cost and complexity of present systems, largely due to the lack of suitable terahertz focal-plane array detectors. Here we report a terahertz focal-plane array that can directly provide the spatial amplitude and phase distributions, along with the ultrafast temporal and spectral information of an imaged object. It consists of a two-dimensional array of ~0.3 million plasmonic photoconductive nanoantennas optimized to rapidly detect broadband terahertz radiation with a high signal-to-noise ratio. We utilized the multispectral nature of the amplitude and phase data captured by these plasmonic nanoantennas to image different objects, including super-resolved etched patterns in a silicon substrate and defects in battery electrodes. By eliminating the need for raster scanning and spatial terahertz modulation, our terahertz focal-plane array offers more than a 1,000-fold increase in the imaging speed compared with the state of the art and potentially suits a broad range of applications in industrial inspection, security screening and medical diagnosis, among others. A terahertz focal-plane array based on a two-dimensional array of plasmonic photoconductive nanoantennas offers high-quality imaging in the terahertz region. [ABSTRACT FROM AUTHOR]

Free-space optical communication becomes challenging when an occlusion blocks the light path. Here, we demonstrate a direct communication scheme, passing optical information around a fully opaque, arbitrarily shaped occlusion that partially or entirely occludes the transmitter's field-of-view. In this scheme, an electronic neural network encoder and a passive, all-optical diffractive network-based decoder are jointly trained using deep learning to transfer the optical information of interest around the opaque occlusion of an arbitrary shape. Following its training, the encoder-decoder pair can communicate any arbitrary optical information around opaque occlusions, where the information decoding occurs at the speed of light propagation through passive light-matter interactions, with resilience against various unknown changes in the occlusion shape and size. We also validate this framework experimentally in the terahertz spectrum using a 3D-printed diffractive decoder. Scalable for operation in any wavelength regime, this scheme could be particularly useful in emerging high data-rate free-space communication systems. Researchers demonstrate robust optical communication around fully opaque occlusions, partially or entirely blocking the light path, using a pair of electronic encoder and passive diffractive decoder that are jointly optimized using deep learning. [ABSTRACT FROM AUTHOR]

Deep Neural Networks (DNNs) have demonstrated great success in many fields such as image recognition and text analysis. However, the ever-increasing sizes of both DNN models and training datasets make deep leaning extremely computation- and memory-intensive. Recently, photonic computing has emerged as a promising technology for accelerating DNNs. While the design of photonic accelerators for DNN inference and forward propagation of DNN training has been widely investigated, the architectural acceleration for equally important backpropagation of DNN training has not been well studied. In this paper, we propose a novel silicon photonic-based backpropagation accelerator for high performance DNN training. Specifically, a general-purpose photonic gradient descent unit named STADIA is designed to implement the multiplication, accumulation, and subtraction operations required for computing gradients using mature optical devices including Mach-Zehnder Interferometer (MZI) and Mircoring Resonator (MRR), which can significantly reduce the training latency and improve the energy efficiency of backpropagation. To demonstrate efficient parallel computing, we propose a STADIA-based backpropagation acceleration architecture and design a dataflow by using wavelength-division multiplexing (WDM). We analyze the precision of STADIA by quantifying the precision limitations imposed by losses and noises. Furthermore, we evaluate STADIA with different element sizes by analyzing the power, area and time delay for photonic accelerators based on DNN models such as AlexNet, VGG19 and ResNet. Simulation results show that the proposed architecture STADIA can achieve significant improvement by 9.7x in time efficiency and 147.2x in energy efficiency, compared with the most advanced optical-memristor based backpropagation accelerator. [ABSTRACT FROM AUTHOR]

The number of parameters in deep neural networks (DNNs) is scaling at about 5× the rate of Moore's Law. To sustain this growth, photonic computing is a promising avenue, as it enables higher throughput in dominant general matrix-matrix multiplication (GEMM) operations in DNNs than their electrical counterpart. However, purely photonic systems face several challenges including lack of photonic memory and accumulation of noise. In this article, we present an electro-photonic accelerator, ADEPT, which leverages a photonic computing unit for performing GEMM operations, a vectorized digital electronic application-specific integrated circuits for performing non-GEMM operations, and SRAM arrays for storing DNN parameters and activations. In contrast to prior works in photonic DNN accelerators, we adopt a system-level perspective and show that the gains while large are tempered relative to prior expectations. Our goal is to encourage architects to explore photonic technology in a more pragmatic way considering the system as a whole to understand its general applicability in accelerating today's DNNs. Our evaluation shows that ADEPT can provide, on average, 5.73× higher throughput per watt compared to the traditional systolic arrays in a full-system, and at least 6.8× and 2.5× better throughput per watt, compared to state-of-the-art electronic and photonic accelerators, respectively. [ABSTRACT FROM AUTHOR]

Intra‐specific trait variation (ITV) plays a role in processes at a wide range of scales from organs to ecosystems across climate gradients. Yet, ITV remains rarely quantified for many ecophysiological traits typically assessed for species means, such as pressure volume (PV) curve parameters including osmotic potential at full turgor and modulus of elasticity, which are important in plant water relations. We defined a baseline "reference ITV" (ITVref) as the variation among fully exposed, mature sun leaves of replicate individuals of a given species grown in similar, well‐watered conditions, representing the conservative sampling design commonly used for species‐level ecophysiological traits. We hypothesized that PV parameters would show low ITVref relative to other leaf morphological traits, and that their intraspecific relationships would be similar to those previously established across species and proposed to arise from biophysical constraints. In a database of novel and published PV curves and additional leaf structural traits for 50 diverse species, we found low ITVref for PV parameters relative to other morphological traits, and strong intraspecific relationships among PV traits. Simulation modeling showed that conservative ITVref enables the use of species‐mean PV parameters for scaling up from spectroscopic measurements of leaf water content to enable sensing of leaf water potential. [ABSTRACT FROM AUTHOR]

Optical deep learning (DL) accelerators have attracted significant interests due to their latency and power advantages. In this article, we focus on incoherent optical designs. A significant challenge is that there is no known solution to perform single-wavelength accumulation (a key operation required for DL workloads) using incoherent optical signals efficiently. Therefore, we devise a hybrid approach, where accumulation is done in the electrical domain, and multiplication is performed in the optical domain. The key technology enabler of our design is the transistor laser, which performs electrical-to-optical and optical-to-electrical conversions efficiently. Through detailed design and evaluation of our design, along with a comprehensive benchmarking study against state-of-the-art RRAM-based designs, we derive the following key results: (1) For a four-layer multilayer perceptron network, our design achieves 115× and 17.11× improvements in latency and energy, respectively, compared to the RRAM-based design. We can take full advantage of the speed and energy benefits of the optical technology because the inference task can be entirely mapped onto our design. (2) For a complex workload (Resnet50), weight reprogramming is needed, and intermediate results need to be stored/re-fetched to/from memories. In this case, for the same area, our design still outperforms the RRAM-based design by 15.92× in inference latency, and 8.99× in energy. [ABSTRACT FROM AUTHOR]

Permutation matrices form an important computational building block frequently used in various fields including, e.g., communications, information security, and data processing. Optical implementation of permutation operators with relatively large number of input–output interconnections based on power-efficient, fast, and compact platforms is highly desirable. Here, we present diffractive optical networks engineered through deep learning to all-optically perform permutation operations that can scale to hundreds of thousands of interconnections between an input and an output field-of-view using passive transmissive layers that are individually structured at the wavelength scale. Our findings indicate that the capacity of the diffractive optical network in approximating a given permutation operation increases proportional to the number of diffractive layers and trainable transmission elements in the system. Such deeper diffractive network designs can pose practical challenges in terms of physical alignment and output diffraction efficiency of the system. We addressed these challenges by designing misalignment tolerant diffractive designs that can all-optically perform arbitrarily selected permutation operations, and experimentally demonstrated, for the first time, a diffractive permutation network that operates at THz part of the spectrum. Diffractive permutation networks might find various applications in, e.g., security, image encryption, and data processing, along with telecommunications; especially with the carrier frequencies in wireless communications approaching THz-bands, the presented diffractive permutation networks can potentially serve as channel routing and interconnection panels in wireless networks. [ABSTRACT FROM AUTHOR]

Presents the information on the MTTS First International Conference on Microwave, Antennas & Circuits. [ABSTRACT FROM AUTHOR]

Strong terahertz (THz) emission from the methylammonium lead iodide (MAPbI3) perovskite semiconductors has been observed following above-bandgap photoexcitation, yet local THz responses of crystalline microstructures are absent. We implement laser THz emission microscope (LTEM), yet-to-be applied to the perovskite semiconductors, as a novel and complementary tool to evaluate the electronic and grain heterogeneity of MAPbI3 thin films. Two MAPbI3 samples with different grain sizes are studied. Using this approach, we show that the one with a larger grain size gives more uniform THz radiation. More significant spatial THz intensity fluctuation is observed for the sample with a smaller grain size. [ABSTRACT FROM AUTHOR]

High-spectral-purity frequency-agile room-temperature sources in the terahertz spectrum are foundational elements for imaging, sensing, metrology, and communications. Here we present a chip-scale optical parametric oscillator based on an integrated nonlinear microresonator that provides broadly tunable single-frequency and multi-frequency oscillators in the terahertz regime. Through optical-to-terahertz down-conversion using a plasmonic nanoantenna array, coherent terahertz radiation spanning 2.8-octaves is achieved from 330 GHz to 2.3 THz, with ≈20 GHz cavity-mode-limited frequency tuning step and ≈10 MHz intracavity-mode continuous frequency tuning range at each step. By controlling the microresonator intracavity power and pump-resonance detuning, tunable multi-frequency terahertz oscillators are also realized. Furthermore, by stabilizing the microresonator pump power and wavelength, sub-100 Hz linewidth of the terahertz radiation with 10−15 residual frequency instability is demonstrated. The room-temperature generation of both single-frequency, frequency-agile terahertz radiation and multi-frequency terahertz oscillators in the chip-scale platform offers unique capabilities in metrology, sensing, imaging and communications. High-spectral-purity frequency-agile room-temperature THz sources are foundational elements for imaging, sensing, metrology, and communications. Here a parametric oscillator-photomixer chip with coherent 2.8-octave tunable THz radiation is achieved. [ABSTRACT FROM AUTHOR]

We present a telecommunication-compatible bias-free photoconductive terahertz emitter composed of a bilayer InAs structure directly grown on a high-resistivity silicon substrate. The bilayer InAs structure includes p+-doped and undoped InAs layers, inducing a strong built-in electric field that enables terahertz generation without requiring any external bias voltage. A large-area plasmonic nanoantenna array is used to enhance and confine optical generation inside the photoconductive region with the highest built-in electric field, leading to the generation of a strong ultrafast photocurrent and broadband terahertz radiation. Thanks to a higher terahertz transmission through the silicon substrate and a shorter carrier lifetime in the InAs layers grown on silicon, higher signal-to-noise ratios are achieved at high terahertz frequencies compared with previously demonstrated bias-free terahertz emitters realized on GaAs. In addition to compatibility with silicon integrated optoelectronic platforms, the presented bias-free photoconductive emitter provides more than a 6 THz radiation bandwidth with more than 100 dB dynamic range when used in a terahertz time-domain spectroscopy system. [ABSTRACT FROM AUTHOR]

92 A. Abbes, P.-K. Lu, P. Nouvel, et al., "280 GHz radiation source driven by a 1064nm continuous-wave dual-frequency vertical external cavity semiconductor laser", in 2021 46th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz), Chengdu, China, IEEE, 2021, pp. 1-2. 93 P.-K. Lu and J. Mona, "A continuous-wave terahertz self-heterodyne spectroscopy system without using short-carrier-lifetime photoconductors", in 2021 46th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz), San Jose, California, US, IEEE, 2021, pp. 1-2. 94 W. Wang, P.-K. Lu, A. K. Vinod, et al.., "High spectral purity chip-scale tunable THz radiation source", in CLEO: Applications and Technology, Chengdu, China, Optical Society of America, 2021, p. ATu2T-3. 95 D. Turan, N. T. Yardimci, and M. Jarrahi, "Plasmonics-enhanced photoconductive terahertz detector pumped by Ytterbium-doped fiber laser", Opt. The radiated power spectral density, however, becomes independent of the recombination time in the high frequency limit, HT ht . Equivalently, beyond the lifetime (or transit-time) cut-off frequency, its value also rolls off as HT ht . [Extracted from the article]

Presents information on the 16th Mediterranean Microwave Symposium. [ABSTRACT FROM PUBLISHER]

Organic π‐conjugated crystals with second‐order optical nonlinearity are considerably attractive materials for diverse terahertz (THz) wave photonics. An overview of the research of organic nonlinear optical (NLO) crystals for THz wave generation, detection, and applications is provided here. First, the status of organic NLO crystals compared to other alternative THz materials is described. Second, the basic theory and requirements for organic THz generators and detectors are explained. Third, with a primary focus on the organic NLO crystals that have been developed during the last decade, various molecular engineering approaches are discussed from the perspective of THz photonics. Finally, various types of high‐power broadband coherent THz sources and their applications in THz nonlinear optics are discussed. [ABSTRACT FROM AUTHOR]

In this article the funding from U.S. Department of Energy was omitted. The original article can be found online at https://doi.org/10.1038/s41467-021-24957-1. [Extracted from the article]

An amplifying quantum-cascade (QC) metasurface, the key component of the QC vertical-external-cavity surface-emitting-laser (VECSEL), is studied as a function of injected current density using reflection-mode terahertz time domain spectroscopy. Nearly perfect absorption is measured at zero bias, which is associated with the transition from the weak to strong coupling condition between the metasurface resonance and an intersubband transition within the QC material. An increase in reflectance is observed as the device is biased, both due to reduction in intersubband loss and the presence of intersubband gain. Significant phase modulation associated with the metasurface resonance is observed via electrical control, which may be useful for electrical tuning of QC-VECSEL. These results provide insight into the interaction between the intersubband QC-gain material and the metasurface and modify the design rules for QC-VECSELs for both biased and unbiased regions. [ABSTRACT FROM AUTHOR]

Surface states generally degrade semiconductor device performance by raising the charge injection barrier height, introducing localized trap states, inducing surface leakage current, and altering the electric potential. We show that the giant built-in electric field created by the surface states can be harnessed to enable passive wavelength conversion without utilizing any nonlinear optical phenomena. Photo-excited surface plasmons are coupled to the surface states to generate an electron gas, which is routed to a nanoantenna array through the giant electric field created by the surface states. The induced current on the nanoantennas, which contains mixing product of different optical frequency components, generates radiation at the beat frequencies of the incident photons. We utilize the functionalities of plasmon-coupled surface states to demonstrate passive wavelength conversion of nanojoule optical pulses at a 1550 nm center wavelength to terahertz regime with efficiencies that exceed nonlinear optical methods by 4-orders of magnitude. Semiconductor surface states often stand in the way of device performance, but here, the authors take advantage of them for wavelength conversion. They present a compact, passive conversion device insensitive to optical alignment by using plasmon-coupled surface states that enable the efficient conversion without nonlinear phenomena. [ABSTRACT FROM AUTHOR]

The three articles in this special section focus on terahertz technology and emerging technologies that offer advanced materials, devices, and systems for the next-generation terahertz applications. [ABSTRACT FROM PUBLISHER]

We demonstrate experimentally the increase of optical-to-terahertz conversion efficiency for GaAs-based photoconductive terahertz emitters. This increase is achieved by preventing device breakdown through series resistors, which act as a current limiter. Pulsed photoexcitation and potential current fluctuations result in heat dissipation leading to local heating, which further increases the current and may lead to device breakdown. We manage to increase the maximum bias field before device breakdown by a factor of 3 under illuminated conditions. For a laser system with 250-kHz repetition rate, the terahertz emission amplitude increases linearly with applied bias field up to 120 kV/cm bias field, which results in 3 times higher signal as compared to the standard device. Furthermore, we have also achieved this expanded breakdown prevention at 78-MHz repetition rate, where an integrated on-chip resistance leads to an enhancement of the terahertz field amplitude by 70%. This simple technique can increase the performance of almost all photoconductive terahertz emitters by using appropriate resistances according to the emitter capacitance and laser repetition rate. [ABSTRACT FROM AUTHOR]

Recent advances in deep learning have been providing non-intuitive solutions to various inverse problems in optics. At the intersection of machine learning and optics, diffractive networks merge wave-optics with deep learning to design task-specific elements to all-optically perform various tasks such as object classification and machine vision. Here, we present a diffractive network, which is used to shape an arbitrary broadband pulse into a desired optical waveform, forming a compact and passive pulse engineering system. We demonstrate the synthesis of various different pulses by designing diffractive layers that collectively engineer the temporal waveform of an input terahertz pulse. Our results demonstrate direct pulse shaping in terahertz spectrum, where the amplitude and phase of the input wavelengths are independently controlled through a passive diffractive device, without the need for an external pump. Furthermore, a physical transfer learning approach is presented to illustrate pulse-width tunability by replacing part of an existing network with newly trained diffractive layers, demonstrating its modularity. This learning-based diffractive pulse engineering framework can find broad applications in e.g., communications, ultra-fast imaging and spectroscopy. Diffractive networks have recently been discussed as an all-optical analogue for performing neural network operations. The authors present a method using deep learning-designed 3D-printed diffractive surfaces to engineer temporal waveforms and perform pulse shaping in the terahertz regime. [ABSTRACT FROM AUTHOR]

Presents a listing of APSS members who were elevated to the stauts of IEEE Fellow. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

As an optical machine learning framework, Diffractive Deep Neural Networks (D2NN) take advantage of data-driven training methods used in deep learning to devise light–matter interaction in 3D for performing a desired statistical inference task. Multi-layer optical object recognition platforms designed with this diffractive framework have been shown to generalize to unseen image data achieving, e.g., >98% blind inference accuracy for hand-written digit classification. The multi-layer structure of diffractive networks offers significant advantages in terms of their diffraction efficiency, inference capability and optical signal contrast. However, the use of multiple diffractive layers also brings practical challenges for the fabrication and alignment of these diffractive systems for accurate optical inference. Here, we introduce and experimentally demonstrate a new training scheme that significantly increases the robustness of diffractive networks against 3D misalignments and fabrication tolerances in the physical implementation of a trained diffractive network. By modeling the undesired layer-to-layer misalignments in 3D as continuous random variables in the optical forward model, diffractive networks are trained to maintain their inference accuracy over a large range of misalignments; we term this diffractive network design as vaccinated D2NN (v-D2NN). We further extend this vaccination strategy to the training of diffractive networks that use differential detectors at the output plane as well as to jointly-trained hybrid (optical-electronic) networks to reveal that all of these diffractive designs improve their resilience to misalignments by taking into account possible 3D fabrication variations and displacements during their training phase. [ABSTRACT FROM AUTHOR]