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1. Single-pixel reconstructive mid-infrared micro-spectrometer

   https://doi.org/10.1364/OE.485934  

   Dong, Kaichen ; Li, Jiachen ; Zhang, Tiancheng ; Gu, Fangda ; Cai, Yuhang ; Gupta, Niharika ; Tang, Kechao ; Javey, Ali ; Yao, Jie ; Wu, Junqiao ( April 2023 , Optics Express)

   Miniaturized spectrometers in the mid-infrared (MIR) are critical in developing next-generation portable electronics for advanced sensing and analysis. The bulky gratings or detector/filter arrays in conventional micro-spectrometers set a physical limitation to their miniaturization. In this work, we demonstrate a single-pixel MIR micro-spectrometer that reconstructs the sample transmission spectrum by a spectrally dispersed light source instead of spatially grated light beams. The spectrally tunable MIR light source is realized based on the thermal emissivity engineered via the metal-insulator phase transition of vanadium dioxide (VO₂). We validate the performance by showing that the transmission spectrum of a magnesium fluoride (MgF₂) sample can be computationally reconstructed from sensor responses at varied light source temperatures. With potentially minimum footprint due to the array-free design, our work opens the possibility where compact MIR spectrometers are integrated into portable electronic systems for versatile applications.
2. Geometric deep optical sensing

   https://doi.org/10.1126/science.ade1220  

   Yuan, Shaofan ; Ma, Chao ; Fetaya, Ethan ; Mueller, Thomas ; Naveh, Doron ; Zhang, Fan ; Xia, Fengnian ( March 2023 , Science)

   BACKGROUND Optical sensing devices measure the rich physical properties of an incident light beam, such as its power, polarization state, spectrum, and intensity distribution. Most conventional sensors, such as power meters, polarimeters, spectrometers, and cameras, are monofunctional and bulky. For example, classical Fourier-transform infrared spectrometers and polarimeters, which characterize the optical spectrum in the infrared and the polarization state of light, respectively, can occupy a considerable portion of an optical table. Over the past decade, the development of integrated sensing solutions by using miniaturized devices together with advanced machine-learning algorithms has accelerated rapidly, and optical sensing research has evolved into a highly interdisciplinary field that encompasses devices and materials engineering, condensed matter physics, and machine learning. To this end, future optical sensing technologies will benefit from innovations in device architecture, discoveries of new quantum materials, demonstrations of previously uncharacterized optical and optoelectronic phenomena, and rapid advances in the development of tailored machine-learning algorithms. ADVANCES Recently, a number of sensing and imaging demonstrations have emerged that differ substantially from conventional sensing schemes in the way that optical information is detected. A typical example is computational spectroscopy. In this new paradigm, a compact spectrometer first collectively captures the comprehensive spectral information ofmore »an incident light beam using multiple elements or a single element under different operational states and generates a high-dimensional photoresponse vector. An advanced algorithm then interprets the vector to achieve reconstruction of the spectrum. This scheme shifts the physical complexity of conventional grating- or interference-based spectrometers to computation. Moreover, many of the recent developments go well beyond optical spectroscopy, and we discuss them within a common framework, dubbed “geometric deep optical sensing.” The term “geometric” is intended to emphasize that in this sensing scheme, the physical properties of an unknown light beam and the corresponding photoresponses can be regarded as points in two respective high-dimensional vector spaces and that the sensing process can be considered to be a mapping from one vector space to the other. The mapping can be linear, nonlinear, or highly entangled; for the latter two cases, deep artificial neural networks represent a natural choice for the encoding and/or decoding processes, from which the term “deep” is derived. In addition to this classical geometric view, the quantum geometry of Bloch electrons in Hilbert space, such as Berry curvature and quantum metrics, is essential for the determination of the polarization-dependent photoresponses in some optical sensors. In this Review, we first present a general perspective of this sensing scheme from the viewpoint of information theory, in which the photoresponse measurement and the extraction of light properties are deemed as information-encoding and -decoding processes, respectively. We then discuss demonstrations in which a reconfigurable sensor (or an array thereof), enabled by device reconfigurability and the implementation of neural networks, can detect the power, polarization state, wavelength, and spatial features of an incident light beam. OUTLOOK As increasingly more computing resources become available, optical sensing is becoming more computational, with device reconfigurability playing a key role. On the one hand, advanced algorithms, including deep neural networks, will enable effective decoding of high-dimensional photoresponse vectors, which reduces the physical complexity of sensors. Therefore, it will be important to integrate memory cells near or within sensors to enable efficient processing and interpretation of a large amount of photoresponse data. On the other hand, analog computation based on neural networks can be performed with an array of reconfigurable devices, which enables direct multiplexing of sensing and computing functions. We anticipate that these two directions will become the engineering frontier of future deep sensing research. On the scientific frontier, exploring quantum geometric and topological properties of new quantum materials in both linear and nonlinear light-matter interactions will enrich the information-encoding pathways for deep optical sensing. In addition, deep sensing schemes will continue to benefit from the latest developments in machine learning. Future highly compact, multifunctional, reconfigurable, and intelligent sensors and imagers will find applications in medical imaging, environmental monitoring, infrared astronomy, and many other areas of our daily lives, especially in the mobile domain and the internet of things. Schematic of deep optical sensing. The n -dimensional unknown information ( w ) is encoded into an m -dimensional photoresponse vector ( x ) by a reconfigurable sensor (or an array thereof), from which w′ is reconstructed by a trained neural network ( n ′ = n and w′   ≈   w ). Alternatively, x may be directly deciphered to capture certain properties of w . Here, w , x , and w′ can be regarded as points in their respective high-dimensional vector spaces ℛ n , ℛ m , and ℛ n ′ .« less
3. High-resolution and compact serpentine integrated grating spectrometer

   https://doi.org/10.1364/JOSAB.423968  

   Brand, Michael ; Zhang, Bohan ; Onural, Deniz ; Al Qubaisi, Kenaish ; Popović, Miloš ; Dostart, Nathan ; Wagner, Kelvin ( June 2021 , Journal of the Optical Society of America B)

   Integrated astrophotonic spectrometers are integrated variants of conventional free-space spectrometers that offer significantly reduced size, weight, and cost and immunity to alignment errors, and can be readily integrated with other astrophotonic instruments such as nulling interferometers. Current integrated dispersive astrophotonic spectrometers are one-dimensional devices such as arrayed waveguide gratings or planar echelle gratings. These devices have been limited to${10}^4$resolving powers and$<<\#comment/>1000$spectral bins due to having limited total optical delay paths and 1D detector array pixel densities. In this paper, we propose and demonstrate a high-resolution and compact astrophotonic serpentine integrated grating (SIG) spectrometer design based on a 2D dispersive serpentine optical phased array. The SIG device combines a 5.2 cm long folded delay line with grating couplers to create a large optical delay path along two dimensions in a compact integrated device footprint. Analogous to free-space crossed-dispersion high-resolution spectrometers, the SIG spectrometer maps spectral content to a 2D wavelength-beam-steered folded-raster emission pattern focused onto a 2D detector array. We demonstrate a SIG spectrometer with$\sim <\#comment/>100\mathrm{k}$resolving power and$\sim <\#comment/>6750$spectral bins, which are approximately an order of magnitude higher than previous integrated photonic designs that operate over a wide bandwidth, in amore »display='inline'>0.4mm2footprint. We measure a Rayleigh resolution of$1.93\pm <\#comment/>0.07\mathrm{G}\mathrm{H}\mathrm{z}$and an operational bandwidth from 1540 nm to 1650 nm. Finally, we discuss refinements of the SIG spectrometer that improve its resolution, bandwidth, and throughput. These results show that SIG spectrometer technology provides a path towards miniaturized, high-resolution spectrometers for applications in astronomy and beyond.

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4. High surface area reverse electrowetting energy harvesting with power conditioning circuitry for self-powered motion sensors

   https://doi.org/10.1117/12.2587541  

   Adhikari, Pashupati R. ; Biswas, Dipon K. ; Tasneem, Nishat T. ; Reid, Russell C. ; Mahbub, Ifana ( April 2021 , SPIE Defense + Commercial Sensing)

   Dutta, Achyut K. ; Balaya, Palani ; Xu, Sheng (Ed.)

   Monitoring human health in real-time using wearable and implantable electronics (WIE) has become one of the most promising and rapidly growing technologies in the healthcare industry. In general, these electronics are powered by batteries that require periodic replacement and maintenance over their lifetime. To prolong the operation of these electronics, they should have zero post-installation maintenance. On this front, various energy harvesting technologies to generate electrical energy from ambient energy sources have been researched. Many energy harvesters currently available are limited by their power output and energy densities. With the miniaturization of wearable and implantable electronics, the size of the harvesters must be miniaturized accordingly in order to increase the energy density of the harvesters. Additionally, many of the energy harvesters also suffer from limited operational parameters such as resonance frequency and variable input signals. In this work, low frequency motion energy harvesting based on reverse electrowetting-ondielectric (REWOD) is examined using perforated high surface area electrodes with 38 µm pore diameters. Total available surface area per planar area was 8.36 cm2 showing a significant surface area enhancement from planar to porous electrodes and proportional increase in AC voltage density from our previous work. In REWOD energy harvesting, high surface areamore »electrodes significantly increase the capacitance and hence the power density. An AC peak-to-peak voltage generation from the electrode in the range from 1.57-3.32 V for the given frequency range of 1-5 Hz with 0.5 Hz step is demonstrated. In addition, the unconditioned power generated from the harvester is converted to a DC power using a commercial off-theshelf Schottky diode-based voltage multiplier and low dropout regulator (LDO) such that the sensors that use this technology could be fully self-powered. The produced charge is then converted to a proportional voltage by using a commercial charge amplifier to record the features of the motion activities. A transceiver radio is also used to transmit the digitized data from the amplifier and the built-in analog-to-digital converter (ADC) in the micro-controller. This paper proposes the energy harvester acting as a self-powered motion sensor for different physical activities for wearable and wireless healthcare devices.« less
5. Disentangling the correlated evolution of body size, life history, and ontogeny in miniaturized chelydroid turtles

   https://doi.org/10.1111/ede.12386  

   Cordero, Gerardo A. ( May 2021 , Evolution & Development)

   Organismal miniaturization is defined by a reduction in body size relative to a large ancestor. In vertebrate animals, miniaturization is achieved by suppressing the energetics of growth. However, this might interfere with reproductive strategies in egg‐laying species with limited energy budgets for embryo growth and differentiation. In general, the extent to which miniaturization coincides with alterations in animal development remains obscure. To address the interplay among body size, life history, and ontogeny, miniaturization in chelydroid turtles was examined. The analyses corroborated that miniaturization in the Chelydroidea clade is underlain by a dampening of the ancestral growth trajectory. There were no associated shifts in the early sequence of developmental transformations, though the relative duration of organogenesis was shortened in miniaturized embryos. The size of eggs, hatchlings, and adults was positively correlated within Chelydroidea. A phylogenetically broader exploration revealed an alternative miniaturization mode wherein exceptionally large hatchlings grow minimally and thus attain diminutive adult sizes. Lastly, it is shown that miniaturized Chelydroidea turtles undergo accelerated ossification coupled with a ~10% reduction in shell bones. As in other vertebrates, the effects of miniaturization were not systemic, possibly owing to opposing functional demands and tissue geometric constraints. This underscores the integrated and hierarchicalmore »nature of developmental systems.

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