Hamamatsu Photonics (Japan)

We propose broadband quantum cascade detectors based on a dual-upper-state active region design. The dual-upper-state concept is known to produce broad gain in quantum cascade lasers and is also expected to provide a wide spectral response for quantum cascade detectors. Compared with quantum cascade detectors employing a bound-to-bound design, dual-upper-state devices are anticipated to exhibit broader bandwidth and can achieve higher responsivity. To validate this, we designed and fabricated quantum cascade detectors for each mid-wave (∼5 μm) and long-wave (∼10 μm) infrared regions, implementing anti-crossed dual-upper states in the main absorbing transition. At 300 K, the fabricated devices exhibited responsivities of ∼13.5 mA/W at λpeak ∼5.1 μm and ∼7.1 mA/W at λpeak ∼10 μm. The measured relative bandwidth of the devices Δλ/λcenter are approximately ∼20.8% and ∼21.1%. These results indicate that the dual-upper-state not only enables a wide spectral response but also may provide enhanced responsivity. The experimental findings are further supported by non-equilibrium Green's function simulations. Overall, our results highlight the potential of the dual-upper-state approach for broadband, high-performance infrared detection.

Abstract This article examines optical fault isolation (OFI) techniques based on heat diffusion for failure analysis of 3D devices, with a focus on thermoreflectance (TR) and laser-induced optical beam-induced resistance change (LI-OBIRCH). TR imaging enables wavelength-selectable probing with structure-dependent thermal coefficients, though performance depends on reducing light source noise. LI-OBIRCH demonstrates 3D analysis capability through a mathematical model and data processing algorithm validated by proof-of-concept experiments on thin 3D test samples. Both techniques show promise for analyzing 3D architectures, including backside power delivery networks and 3D NAND flash memories. Ongoing development addresses physics-related and algorithmic challenges to advance failure analysis capabilities for next-generation semiconductor devices.

Accurate time-of-flight (TOF) information is crucial for improving image reconstruction quality in positron emission tomography (TOF-PET). However, the real event signals from the detector are often affected by time delays caused by the broadening of single-photon responses, which are further exacerbated by statistical fluctuations inherent in non-ideal SiPMs, such as dark counts and limited photon detection efficiency, as well as by backend electronics noise, including dark current noise, leading to reduced TOF information accuracy. In this work, we propose an unsupervised framework based on a Cycle-Consistent Generative Adversarial Network (CycleGAN) to learn bidirectional mappings between real event signals and event signals that eliminate the influence of dark current noise from the backend electronics, enables the reshaping of real event signals into event signals with improved TOF information accuracy. Then we utilize Geant4 to simulate and generate virtual event signals that closely match the idealized event signals, with an average coincidence timing resolution (CTR) improvement of 108 ps over the real event signals. After applying CFD timing to the real event signals and reshaped event signals, the results show significant improvements in CTR, with average improvements of 41 ps (15.1 \%), 81 ps(26.0 \%), and 79 ps (25.5 i \%) compared to traditional methods such as Constant Fraction Discrimination (CFD), Leading Edge Discrimination (LED), and Rise Time Compensated (ARC). And demonstrates a highly uniform TOF distribution across all gamma source positions, with no edge bias issue typically observed in supervised CNN training at boundary positions.

Despite its versatility and high chemical specificity, conventional nuclear magnetic resonance (NMR) spectroscopy is limited in measurement throughput due to the need for high-homogeneity magnetic fields, necessitating sequential sample analysis, and expensive devices. Here, we propose a multichannel NMR device that addresses these limitations by leveraging the zero-to ultralow-field (ZULF) regime, where simultaneous detection of multiple samples is carried out via an array of compact optically pumped magnetometers (OPMs). A magnetic field is used only for prepolarization, permitting the use of large-bore, high-field, inhomogeneous magnets that can accommodate multiple samples concurrently. Through systematic improvements, we demonstrate sensitive, high-resolution ZULF NMR spectroscopy with sensitivity comparable to benchtop 13C NMR systems. The spectroscopy remains robust without the need for field shimming for periods on the order of weeks. We show the detection of ZULF NMR signals from organic molecules without isotopic enrichment, and demonstrate the parallelized detection of three distinct samples simultaneously as a proof-of-concept, with the ability to scale further to over 100 channels at a cost comparable to traditional liquid state NMR systems. This work sets the stage for using multichannel “NMR camera” devices for inline reaction monitoring, robotic chemistry, quality control, and high-throughput assays.

Convergence of the block iterative method in image reconstruction for positron emission tomography (PET) requires careful control of relaxation parameters, which is a challenging task. The automatic determination of relaxation parameters for list-mode reconstructions also remains challenging. Therefore, a different approach would be desirable. In this study, we propose a list-mode maximum-likelihood Dykstra-like splitting PET reconstruction (LM-MLDS) that reduces the limit-cycle amplitude by adding the distance from an initial image as a penalty term into an objective function. LM-MLDS uses a two-step approach because its performance depends on the quality of the initial image. The first step uses a uniform image as the initial image, whereas the second step uses a reconstructed image after one main iteration as the initial image. In a simulation study, LM-MLDS provided a better tradeoff curve between noise and contrast than the other methods. In a clinical study, LM-MLDS removed the false hotspots at the edge of the axial field of view and improved the image quality of slices covering the top of the head to the cerebellum. List-mode proximal splitting reconstruction is useful not only for optimizing nondifferential functions but also for mitigating the limit-cycle phenomenon in block iterative methods.

We demonstrate a pairwise transmission concept which breaks the fiber dispersion barrier in multilane IM-DD systems by joint optoelectronic coding across multiple lanes. Low-complexity C-band 224Gb/s (112Gb/s/lane) up-to-80km transmissions with different link configurations are investigated.