The sudden rise of the worldwide severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in early 2020 has called into drastic action measures to perform instant detection and reduce the rate of spread. Common clinical and nonclinical diagnostic testing methods have been partially effective in satisfying the increasing demand for fast detection point-of-care (POC) methods to slow down further spread. However, accurate point-of-risk diagnosis of this emerging viral infection is paramount as the need for simultaneous standard operating procedures and symptom management of SARS-CoV-2 will be the norm for years to come. A sensitive, cost-effective biosensor with mass production capability is crucial until a universal vaccination becomes available. Optical biosensors can provide a noninvasive, extremely sensitive rapid detection platform with sensitivity down to ∼67 fg/ml (1 fM) concentration in a few minutes. These biosensors can be manufactured on a mass scale (millions) to detect the COVID-19 viral load in nasal, saliva, urine, and serological samples, even if the infected person is asymptotic. Methods investigated here are the most advanced available platforms for biosensing optical devices that have resulted from the integration of state-of-the-art designs and materials. These approaches include, but are not limited to, integrated optical devices, plasmonic resonance, and emerging nanomaterial biosensors. The lab-on-chip platforms examined here are suitable not only for SARS-CoV-2 spike protein detection but also for other contagious virions such as influenza and Middle East respiratory syndrome (MERS).

Vertical‐cavity surface‐emitting lasers (VCSELs) have emerged as a vital approach for realizing energy‐efficient and high‐speed optical interconnects in the data centers and supercomputers. Indeed, VCSELs are the most suitable mass production lasers in terms of cost‐effectiveness and reliability. However, there are still key challenges that prevent achieving modulation speeds beyond 30s GHz. Here, we propose a novel VCSEL design of a hexagonal transverse‐coupled‐cavity adiabatically coupled through a central cavity. Following this scheme, we show a prototype demonstrating a 3‐dB roll‐off modulation bandwidth of 45 GHz, which is five times greater than a conventional VCSEL fabricated on the same epiwafer structure. This design harnesses the Vernier effect to increase the laser’s aperture and therefore is capable of maintaining single‐mode operation of the laser for high injection currents, hence extending the dynamic roll‐off point and offering increases power output. Simultaneously, extending both the laser modulation speed and output power for this heavily deployed class of lasers opens up new opportunities and fields of use ranging from data‐comm to sensing, automotive, and photonic artificial intelligence systems.

With the increasing need for large volumes of data processing, transport, and storage, optimizing the trade-off between high-speed and energy consumption in today’s optoelectronic devices is getting increasingly difficult. Heterogeneous material integration into silicon- and nitride-based photonics has showed high-speed promise, albeit at the expense of millimeter-to centimeter-scale footprints. The hunt for an electro-optic modulator that combines high speed, energy efficiency, and compactness to support high component density on-chip continues. Using a double-layer graphene optical modulator integrated on a Silicon photonics platform, we are able to achieve 60 GHz speed (3 dB roll-off), micrometer compactness, and efficiency of 2.25 fJ/bit in this paper. The electro-optic response is boosted further by a vertical distributed-Bragg-reflector cavity, which reduces the driving voltage by about 40 times while maintaining a sufficient modulation depth (5.2 dB/V). Modulators that are small, efficient, and quick allow high photonic chip density and performance, which is critical for signal processing, sensor platforms, and analog- and neuromorphic photonic processors.

Thanks to the unique molecular fingerprints in the mid‐infrared spectral region, absorption spectroscopy in this regime has attracted widespread attention in recent years. Contrary to commercially available infrared spectrometers, which are limited by being bulky and cost‐intensive, laboratory‐on‐chip infrared spectrometers can offer sensor advancements including raw sensing performance in addition to utilization such as enhanced portability. Several platforms have been proposed in the past for on‐chip ethanol detection. However, selective sensing with high sensitivity at room temperature has remained a challenge. Here, we experimentally demonstrate an on‐chip ethyl alcohol sensor based on a holey photonic crystal waveguide on silicon on insulator‐based photonics sensing platform offering an enhanced photoabsorption thus improving sensitivity. This is achieved by designing and engineering an optical slow‐light mode with a high group‐index of n _g = 73 and a strong localization of the modal power in analyte, enabled by the photonic crystal waveguide structure. This approach includes a codesign paradigm that uniquely features an increased effective path length traversed by the guided wave through the to‐be‐sensed gas analyte. This PIC‐based lab‐on‐chip sensor is exemplary, spectrally designed to operate at the center wavelength of 3.4 μm to match the peak absorbance for ethanol. However, the slow‐light enhancement concept is universal offering to cover a wide design‐window and spectral ranges towards sensing a plurality of gas species. Using the holey photonic crystal waveguide, we demonstrate the capability of achieving parts per billion levels of gas detection precision. High sensitivity combined with tailorable spectral range along with a compact form‐factor enables a new class of portable photonic sensor platforms when integrated with quantum cascade laser and detectors.

Electro-optic modulators provide a key function in optical transceivers and increasingly in photonic programmable application-specific integrated circuits (ASICs) for machine learning and signal processing. However, both foundry-ready silicon-based modulators and conventional material-based devices utilizing lithium-niobate fall short in simultaneously providing high chip packaging density and fast speed. Current-driven ITO-based modulators have the potential to achieve both enabled by efficient light–matter interactions. Here, we introduce micrometer-compact Mach–Zehnder interferometer (MZI)-based modulators capable of exceeding 100 GHz switching rates. Integrating ITO-thin films atop a photonic waveguide, one can achieve an efficient V π L = 0.1 V mm, spectrally broadband, and compact MZI phase shifter. Remarkably, this allows integrating more than 3500 of these modulators within the same chip area as only one single silicon MZI modulator. The modulator design introduced here features a holistic photonic, electronic, and RF-based optimization and includes an asymmetric MZI tuning step to optimize the extinction ratio (ER)-to-insertion loss (IL) and dielectric thickness sweep to balance the trade-offs between ER and speed. Driven by CMOS compatible bias voltage levels, this device is the first to address next-generation modulator demands for processors of the machine intelligence revolution, in addition to the edge and cloud computing demands as well as optical transceivers alike.

Advancements in optical communications have increasingly focused on leveraging spatial-structured beams such as orbital angular momentum (OAM) beams for high-capacity data transmission. Conventional electronic convolutional neural networks exhibit constraints in efficiently demultiplexing OAM signals. Here, we introduce a hybrid optical-electronic convolutional neural network that is capable of completing Fourier optics convolution and realizing intensity-recognition-based demultiplexing of multiplexed OAM beams under variable simulated atmospheric turbulent conditions. The core part of our demultiplexing system includes a 4F optics system employing a Fourier optics convolution layer. This optical spatial-filtering-based convolutional neural network is utilized to realize the training and demultiplexing of the 4-bit OAM-coded signals under simulated atmospheric turbulent conditions. The current system shows a demultiplexing accuracy of 72.84% under strong turbulence scenarios with 3.2 times faster training time than all electronic convolutional neural networks.