Neuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics for machine learning algorithms, such ...as neural networks of various types. Integrated photonic networks are particularly powerful in performing analog computing of matrix-vector multiplication (MVM) as they afford unparalleled speed and bandwidth density for data transmission. Incorporating nonvolatile phase-change materials in integrated photonic devices enables indispensable programming and in-memory computing capabilities for on-chip optical computing. Here, we demonstrate a multimode photonic computing core consisting of an array of programable mode converters based on on-waveguide metasurfaces made of phase-change materials. The programmable converters utilize the refractive index change of the phase-change material Ge
Sb
Te
during phase transition to control the waveguide spatial modes with a very high precision of up to 64 levels in modal contrast. This contrast is used to represent the matrix elements, with 6-bit resolution and both positive and negative values, to perform MVM computation in neural network algorithms. We demonstrate a prototypical optical convolutional neural network that can perform image processing and recognition tasks with high accuracy. With a broad operation bandwidth and a compact device footprint, the demonstrated multimode photonic core is promising toward large-scale photonic neural networks with ultrahigh computation throughputs.
Abstract
Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his ...discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.
We report a unique case involving a pure invasive micropapillary carcinoma (IMPC) of the nipple. At the same time, no cancer cells were found in other glands of the breast. There is no documented ...literature in the world on the micropapillary carcinoma of the nipple. A 43-year-old female detected a lump growing on her nipple since December 2015; however, she did not seek treatment before the lump enlarged. The patient presented at our hospital and underwent resection of the lump on 14 December 2016. Pathologic examination of the surgical specimen revealed IMPC involving the nipple. The patient underwent unilateral radical surgery. The pathologic examination of the specimen revealed normal breast tissue without cancer cells. Herein, we discuss the characteristics, diagnosis, and treatment of IMPC, hoping to provide further insight for clinicians and pathologists.
Data-centric applications are pushing the limits of energy-efficiency in today's computing systems, including those based on phase-change memory (PCM). This technology must achieve low-power and ...stable operation at nanoscale dimensions to succeed in high-density memory arrays. Here we use a novel combination of phase-change material superlattices and nanocomposites (based on Ge
Sb
Te
), to achieve record-low power density ≈ 5 MW/cm
and ≈ 0.7 V switching voltage (compatible with modern logic processors) in PCM devices with the smallest dimensions to date (≈ 40 nm) for a superlattice technology on a CMOS-compatible substrate. These devices also simultaneously exhibit low resistance drift with 8 resistance states, good endurance (≈ 2 × 10
cycles), and fast switching (≈ 40 ns). The efficient switching is enabled by strong heat confinement within the superlattice materials and the nanoscale device dimensions. The microstructural properties of the Ge
Sb
Te
nanocomposite and its high crystallization temperature ensure the fast-switching speed and stability in our superlattice PCM devices. These results re-establish PCM technology as one of the frontrunners for energy-efficient data storage and computing.
High‐throughput experimental approaches to rapidly develop new materials require high‐throughput data analysis methods to match. Spectroscopic ellipsometry is a powerful method of optical properties ...characterization, but for unknown materials and/or layer structures the data analysis using traditional methods of nonlinear regression is too slow for autonomous, closed‐loop, high‐throughput experimentation. Herein, three methods (termed spectral, piecewise, and pointwise) of spectroscopic ellipsometry data analysis based on deep learning are introduced and studied. After initial training, the incremental time for inferring optical properties can be a thousand times faster than traditional methods. Results for multilayer sample structures with optically isotropic materials are presented, appropriate for high‐throughput studies of thin films of phase‐change materials such as GeSbTe (GST) alloys. Results for studies on highly birefringent layered materials are also presented, exemplified by the transition metal dichalcogenide MoS2. How the materials under test and the experimental objectives may guide the choice of analysis methods are discussed. The utility of our approach is demonstrated by analyzing data measured on a composition spread of GeSbTe phase‐change alloys containing 177 distinct compositions, and identifying the composition with optimal phase‐change figure of merit in only 1.4 s of analysis time.
Spectroscopic ellipsometry is a powerful and data‐rich metrology to characterize materials, devices, and manufacturing processes. However, traditional data analysis tends to be too slow for rapid feedback. Deep learning methods are developed to quickly and accurately analyze spectroscopic ellipsometry data. The efficacy of the methods are demonstrated using data from high‐throughput synthesis of phase‐change materials for photonics.
Reversible, nonvolatile, and pronounced refractive index modulation is an unprecedented combination of properties enabled by chalcogenide phase-change materials (PCMs). This combination of properties ...makes PCMs a fast-growing platform for active, low-energy nanophotonics, including tunability to otherwise passive thin-film optical coatings. Here, we integrate the PCM Sb2Se3 into a novel four-layer thin-film optical coating that exploits photonic Fano resonances to achieve tunable structural colors in both reflection and transmission. We show, contrary to traditional coatings, that Fano-resonant optical coatings (FROCs) allow for achieving transmissive and reflective structures with narrowband peaks at the same resonant wavelength. Moreover, we demonstrate asymmetric optical response in reflection, where Fano resonance and narrow-band filtering are observed depending upon the light incidence side. Finally, we use a multi-objective inverse design via machine learning (ML) to provide a wide range of solution sets with optimized structures while providing information on the performance limitations of the PCM-based FROCs. Adding tunability to the newly introduced Fano-resonant optical coatings opens various applications in spectral and beam splitting, and simultaneous reflective and transmissive displays, diffractive objects, and holograms.
Mastoscopic surgery is proven to have lower incidence of postoperative complications and better postoperative recovery than traditional breast cancer surgery. This study aimed to examine the ...feasibility of mastoscopic modified radical mastectomy (MRM) with skin nipple-areola preservation under air cavity-free suspension hook and stage I silicone prosthesis implantation (SMALND) compared with routine MRM.
This was a retrospective study of patients who underwent MRM for breast cancer at the Shengjing Hospital Affiliated to China Medical University between January 1, 2019, and June 30, 2019. Surgical outcomes, complications, satisfaction, and quality of life (Functional Assessment of Cancer Therapy-Breast FACT-B Chinese version) were compared between the two groups.
A total of 87 patients were enrolled, with 30 underwent SMALND and 57 underwent routine MRM. The intraoperative blood loss in the SMALND group was lower than in the control group (165.3±44.1 vs. 201.4±52.7 ml, P=0.001), the operation time was longer (220.5±23.9 vs. 155.6±9.2 min, P<0.001), daily axillary drainage volume was smaller (20.2±3.6 vs. 24.1±3.0 ml, P<0.001), daily subcutaneous drainage volume was smaller (15.5±2.3 vs. 19.3±3.5 ml, P<0.001), the discharge time was shorter (7.5±1.6 vs. 9.0±1.8 days, P<0.001), and FACT-B scores were higher (83.8±5.6 vs. 72.1±4.6, P<0.001). The overall satisfaction was higher in the SMALND group than in the controls (76.7% vs. 54.4%, P=0.041). Compared with the controls, the occurrence rates of nipple and flap necrosis, upper limb edema, and paraesthesia in the SMALND group were lower within 6 months (all P<0.05).
Compared with traditional MRM, SMALND had better surgical outcomes, higher satisfaction, higher quality of life, and lower complication rates.
Chemical vapor deposition (CVD) is one of the most versatile techniques for the controlled synthesis of functional nanomaterials. When multiple precursors are induced, the CVD process often gives ...rise to the growth of doped or alloy compounds. In this work, we demonstrate the self-assembly of a variety of ‘phase-separated’ functional nanostructures from a single CVD in the presence of various precursors. In specific, with silicon substrate and powder of Mn and SnTe as precursors, we achieved self-organized nanostructures including Si/SiO
x
core-shell nanowire heterostructures both with and without embedded manganese silicide particles, Mn
11
Si
19
nanowires, and SnTe nanoplates. The Si/SiO
x
core-shell nanowires embedded with manganese silicide particles were grown along the direction of the crystalline Si via an Au-catalyzed vapor-liquid-solid process, in which the Si and Mn vapors were supplied from the heated silicon substrates and Mn powder, respectively. In contrast, direct vapor-solid deposition led to particle-free -oriented Si/SiO
x
core-shell nanowires and -oriented Mn
11
Si
19
nanowires, a promising thermoelectric material. No Sn or Te impurities were detected in these nanostructures down to the experimental limit. Topological crystalline insulator SnTe nanoplates with dominant {100} and {111} facets were found to be free of Mn (and Si) impurities, although nanoparticles and nanowires containing Mn were found in the vicinity of the nanoplates. While multiple-channel transport was observed in the SnTe nanoplates, it may not be related to the topological surface states due to surface oxidation. Finally, we carried out thermodynamic analysis and density functional theory calculations to understand the ‘phase-separation’ phenomenon and further discuss general approaches to grow phase-pure samples when the precursors contain residual impurities.
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