Mona Jarrahi

Image projection systems must be efficient in data storage, computation and transmission while maintaining a large space-bandwidth-product (SBP) at their output. Here, we introduce a hybrid image projection system that achieves extended depth-of-field (DOF) with improved resolution, combining a convolutional neural network (CNN)-based digital encoder with an all-optical diffractive decoder. A CNN-based encoder compresses input images into compact phase representations, which are subsequently displayed by a low-resolution (LR) projector and processed by an analog diffractive decoder for all-optical image reconstruction. This optical decoder is completely passive, designed to synthesize pixel super-resolved image projections that feature an extended DOF while eliminating the need for additional power consumption for super-resolved image reconstruction. Our pixel super-resolution (PSR) image projection system demonstrates high-fidelity image synthesis over an extended DOF of ≥250 λ , where λ is the illumination wavelength, concurrently offering up to ~16-fold SBP improvement at each lateral plane. The feasibility of this approach is validated through proof-of-concept experiments conducted in both the terahertz and visible spectral regions, demonstrating the scalability of our hybrid system across different wavelengths. This image projection architecture can reduce data storage and transmission requirements for display systems without imposing additional power constraints on the optical decoder. Beyond extended DOF PSR image projection, the underlying principles of this approach can be extended to various applications, including optical metrology and microscopy.

Terahertz waves hold immense potential across diverse fields, including healthcare monitoring, biomedical imaging, precision navigation, high-speed communication, security screening, industrial quality control, and space exploration. However, the widespread adoption of terahertz technology has been hindered by the bulky, complex, and costly nature of existing systems. Here, we demonstrate gain-enhanced interband photomixing in quantum well (QW) PIN photodiodes as an efficient mechanism for frequency-tunable terahertz generation and detection, achieving significant improvements in power efficiency and sensitivity over the state-of-the-art. QWs embedded in PIN photodiodes—key elements of commercially available photonic integrated circuits (PICs)—enable monolithic integration of lasers, semiconductor optical amplifiers (SOAs), modulators, filters, demultiplexers, and other passive optical components. By establishing QW PIN photodiodes as the foundation of a Monolithically Integrated Terahertz Optoelectronic (MITO) platform, this work paves the way for compact, scalable terahertz optoelectronic systems with applications in high-speed data transfer, spectroscopy, and hyperspectral imaging. This advancement positions terahertz technology for widespread use, facilitating practical applications across remote sensing, communications, and medical diagnostics within portable devices.

Detecting concealed chemicals and explosives remains a critical challenge in global security. Terahertz time-domain spectroscopy (THz-TDS) offers a promising non-invasive and stand-off detection technique owing to its ability to penetrate optically opaque materials without causing ionization damage. While many chemicals exhibit distinct spectral features in the terahertz range, conventional terahertz-based detection methods often struggle in real-world environments, where variations in sample geometry, thickness, and packaging can lead to inconsistent spectral responses. In this study, we present a chemical imaging system that integrates THz-TDS with deep learning to enable accurate pixel-level identification and classification of different explosives. Operating in reflection mode and enhanced with plasmonic nanoantenna arrays, our THz-TDS system achieves a peak dynamic range of 96 dB and a detection bandwidth of 4.5 THz, supporting practical, stand-off operation. By analyzing individual time-domain pulses with deep neural networks, the system exhibits strong resilience to environmental variations and sample inconsistencies. Blind testing across eight chemicals—including pharmaceutical excipients and explosive compounds—resulted in an average classification accuracy of 99.42% at the pixel level. Notably, the system maintained an average accuracy of 88.83% when detecting explosives concealed under opaque paper coverings, demonstrating its robust generalization capability. These results highlight the potential of combining advanced terahertz spectroscopy with neural networks for highly sensitive and specific chemical and explosive detection in diverse and operationally relevant scenarios.

Quantum well (QW) structures are widely used in lasers, semiconductor optical amplifiers, and modulators, enabling their monolithic integration on the same substrate. As optoelectronic systems evolve to meet the growing bandwidth demands in the terahertz regime, a deep understanding of ultrafast carrier dynamics in QW structures becomes essential. We introduce a comprehensive model to analyze the ultrafast dynamics of interband photo-excited carriers in QW p–i–n structures and to calculate their frequency response. This model characterizes the entire photocarrier transport process, including carrier escape from QWs and movement across heterojunction interfaces. Additionally, we outline theoretical methods for calculating carrier escape times from both QWs and heterojunction interfaces. Using a GaAs/AlGaAs QW p–i–n structure as a case study, we discuss the effects of carrier escape times from QWs and heterojunction interfaces, as well as carrier transit time through the intrinsic region, on the frequency response of QW p–i–n structures.

We demonstrate a high-speed fiber-coupled terahertz time-domain spectroscopy (THz-TDS) system using a bias-free plasmonic terahertz source. The system is powered by a compact, low-power femtosecond fiber laser oscillator that drives a dual-branch fiber amplifier to pump and probe a plasmonic terahertz source and detector, respectively. A polarization-maintaining, fiber-coupled optical delay line is employed to precisely control the temporal offset between the pump and probe beams through a compact, robust platform. Operating in the self-similar amplification regime, the fiber amplifiers are engineered to mitigate nonlinear pulse distortion and dispersion while delivering robust, high-energy pulses at a 1560-nm wavelength, with an output power exceeding 326 mW. In reflection mode, the THz-TDS system achieves a peak dynamic range of 81 dB and a bandwidth greater than 3.5 THz at a scan rate of 3.75 Hz. The utility of the system for rapid sensing is further demonstrated by quantifying the porosity and density of pharmaceutical tablets through measurements of their physical thickness and effective refractive index.

Snell’s law dictates the phenomenon of light refraction at the interface between two media. Here, we demonstrate arbitrary programming of light refraction through an engineered material where the direction of the output wave can be set independently for different directions of the input wave, covering arbitrarily selected permutations of light refraction between the input and output apertures. Formed by a set of cascaded transmissive layers with optimized phase profiles, this refractive function generator (RFG) spans only a few tens of wavelengths in the axial direction. In addition to monochrome RFG designs, we also report wavelength-multiplexed refractive functions, where a distinct refractive function is implemented at each wavelength through the same engineered material volume, i.e., the permutation of light refraction is switched from one desired function to another function by changing the illumination wavelength. As experimental proofs of concept, we demonstrate permutation and negative refractive functions at the terahertz part of the spectrum using 3D-printed materials. Arbitrary programming of refractive functions enables new design capabilities for optical materials, devices and systems.

We introduce universal diffractive waveguide designs that can match the performance of conventional dielectric waveguides and achieve various functionalities. Optimized using deep learning, diffractive waveguides can be cascaded to form any desired length and are comprised of transmissive diffractive surfaces that permit the propagation of desired modes with low loss and high mode purity. In addition to guiding the targeted modes through cascaded diffractive units, we also developed various waveguide components and introduced bent diffractive waveguides, rotating the direction of mode propagation, as well as spatial and spectral mode filtering and mode splitting diffractive waveguide designs, and mode-specific polarization control. This framework was experimentally validated in the terahertz spectrum to selectively pass certain spatial modes while rejecting others. Without the need for material dispersion engineering diffractive waveguides can be scaled to operate at different wavelengths, including visible and infrared spectrum, covering potential applications in, e.g., telecommunications, imaging, sensing and spectroscopy.

We demonstrate multi-tone terahertz generation using a Fabry Perot laser, semiconductor optical amplifier, and photomixer fabricated on the same quantum well substrate, showing potential use of quantum well structures for monolithically integrated terahertz optoelectronic systems.

We present terahertz generation and detection spanning 0.1–0.5 THz via photomixing in GaAs/AlGaAs quantum well structures. We achieve significantly higher power efficiency and detection sensitivity compared to current state-of-the-art through a monolithically integrated platform.

We present a lock-in detection technique that enhances the signal-to-noise ratio of captured spectra in plasmonic heterodyne terahertz spectrometers, achieving a noise reduction rate of 10 dB per decade of integration time while preserving spectral information.