ACS Photonics最新文献

Topological materials have attracted considerable attention for their potential in broadband and fast photoresponses, particularly in the infrared regime. However, the high carrier concentration in these systems often leads to rapid recombination of photogenerated carriers, limiting the photoresponsivity. Here, we demonstrate that Sb doping in MnBi₂Te₄ effectively reduces carrier concentration and suppresses electron–hole recombination, thereby significantly improving the optoelectronic performance across the visible to mid-infrared spectra. The optimally doped Mn(Bi_0.82Sb_0.18)₂Te₄ photodetector achieves a responsivity of 3.02 mA W^–1 with a response time of 18.5 μs at 1550 nm, and 0.795 mA W^–1 with a response time of 9.0 μs at 4 μm. These values represent nearly 2 orders of magnitude improvement compared to undoped MnBi₂Te₄. Our results highlight band engineering as an effective strategy to enhance the infrared performance of topological material-based photodetectors, opening new avenues for high-sensitivity infrared detection.

Positron emission tomography (PET) is the most sensitive biomedical imaging modality for noninvasively detecting and visualizing positron-emitting radiopharmaceuticals within a subject. In PET, measuring the time-of-flight (TOF) information for each pair of 511 keV annihilation photons improves effective sensitivity but requires high timing resolution. Hybrid materials that emit both scintillation and Cherenkov photons, such as bismuth germanate, recently offer the potential for more precise timing information from Cherenkov photons while maintaining adequate energy resolution from scintillation photons. However, a significant challenge in using such hybrid materials for TOF PET applications lies in the event-dependent timing spread caused by the mixed detection of Cherenkov and scintillation photons due to relatively lower production of Cherenkov photons. This study introduces an innovative approach by segmenting silicon photomultiplier (SiPM) pixels coupled to a single crystal, rather than using traditional SiPMs that are as large as or larger than the crystals they read. We demonstrated that multiple timestamps and photon counts obtained from the segmented SiPM can classify events by providing temporal photon density, effectively addressing this challenge. The approach and findings would lead to new opportunities in applications that require precise timing and photon counting.

Optical beam shifts in two-dimensional crystals have been measured until now on samples deposited on some substrates. In these cases, the reflected beam is the linear superposition of the atomic crystal and the substrate contribution. By immersion of a monolayer MoS₂ in polydimethylsiloxane, a transparent dielectric material, we can measure the light reflected from the two-dimensional material only. In conjunction with a weak measurement amplification scheme, this allows us to observe for the first time the role of the out-of-plane susceptibility on the angular Goos-Hänchen shift from a two-dimensional material.

During machine visual perception, the optical signal from a scene is transferred into the electronic domain by detectors in the form of image data, which are then processed for the extraction of visual information. In noisy environments, such as a thermal imaging system, however, the neural performance faces a significant bottleneck due to the inherent degradation of data quality upon noisy detection. Here, we propose a concept of optical signal processing before detection to address this issue. We demonstrate that spatially redistributing optical signals through a properly designed linear transformer can enhance the detection noise resilience of visual perception, as benchmarked with MNIST classification. A quantitative analysis of the relationship between signal concentration and noise robustness supports our idea with its practical implementation in an incoherent imaging system. This compute-first detection scheme can advance infrared machine vision technologies for industrial and defense applications.

As information technology advances and data volumes grow rapidly, there is an increasing requirement for information security and throughput in image transmission systems. Diffractive neural networks (DNNs), a novel all-optical information processing paradigm, offer several advantages including high-speed processing, low energy consumption, and high spatial utilization. These networks also leverage the powerful reverse-design capabilities of deep learning methods, enabling the efficient implementation of various image information encoding techniques. The discrete cosine transform (DCT), a well-established technology widely used in image encoding, shares features with DNNs, such as linear operations, spatial–frequency domain conversions, and high parallelism. This research focuses on building an all-optical DCT processor based on the DNN architecture (DCT–DNN). Testing revealed that this processor performed DCT operations on random matrices and achieved DCT-based compression on specific data sets. Additionally, the DCT with a block for large-sized images was validated. The DCT–DNN, with its high speed and low energy consumption, can be integrated with other complex optoelectronic computing systems to serve as a general computing device for computational acceleration. Furthermore, it can be combined with data transmission systems or directly integrated into image information collection systems to encode and transmit front-end collected information. This makes it a valuable tool for data processing, encryption, and transmission applications.