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Mustafa Hammood

Publications and source records attributed to Mustafa Hammood.

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Optoelectronic Physical Unclonable Functions and Reservoir-Inspired Computation with Low Symmetry Integrated Photonics

Emerging applications of photonics in computing, sensing, and security increasingly demand complex input-output behaviors, including highly nonlinear transformations of optical signals. Traditional photonic systems rely on highly structured components with symmetric geometries and low-entropy modal responses to achieve predictable and analytically describable behavior. To achieve expressive functionality, this paradigm often requires large networks of fabrication-sensitive interferometers or resonators and substantial hardware error correction to restore deterministic operation. Here we demonstrate an alternative paradigm rooted in low-symmetry, disordered integrated photonic circuits, which provide intrinsically enhanced modal diversity and spectral complexity, enabling highly nonlinear transformations of input signals into information-rich outputs. Our devices, physically unclonable moire quasicrystal interferometers integrated on a silicon photonics platform, exhibit aperiodic and reconfigurable spectral responses and are characterized by analyticity breaking and erasable mutual information. Using dynamic thermo-optic control to drive their complex spectral dynamics, we demonstrate that these devices function as reconfigurable physical unclonable functions (rPUFs). We also highlight their ability to perform high-dimensional input-output transformations, emulating reservoir-inspired information processing in a compact photonic platform. This work bridges the gap between engineered and natural complexity in photonic systems, revealing new opportunities for scalable, energy-efficient, and information-dense optoelectronics with applications in secure communications, hardware security, advanced sensing, and optical information processing. Our results establish low-symmetry integrated photonics as a powerful resource for complex signal manipulation in photonic systems.

physics.optics

GHz-rate optical phase shift in light matter interaction-engineered, silicon-ferroelectric nematic liquid crystals

Organic electro-optic materials have demonstrated promising performance in developing electro-optic phase shifters. Their integration with other silicon photonic processes, nanofabrication complexities, and durability remains to be developed. While the required poling step in electro-optic polymers limits their potential and utilization on a large scale, devices made of paraelectric nematic liquid crystals suffer from slow bandwidth. In ferroelectric nematic liquid crystals, we report an additional GHz-fast phase shift that ultimately allows for significant second-order nonlinear optical coefficients related to the Pockels effect. It avoids poling issues and can pave the way for hybrid silicon-organic systems with CMOS-foundry compatibility. We report DC and AC modulation efficiencies of $\approx$~0.25 V$\cdot$mm (from liquid crystal orientation) and $\approx$~25.7 V$\cdot$mm (from Pockels effect), respectively, an on-chip insertion loss of $\approx$~2.6 dB, and an electro-optic bandwidth of $f_\text{-6dB}$>4.18 GHz, employing improved light-matter interaction in a waveguide architecture that calls for only one lithography step.

physics.optics

Fabrication-Aware Inverse Design For Shape Optimization

Inverse design (ID) is a computational method that systematically explores a design space to find optimal device geometries based on specific performance criteria. In silicon photonics, ID often leads to devices with design features that degrade significantly due to the fabrication process, limiting the applicability of these devices in scalable silicon photonic fabrication. We demonstrate a solution to this performance degradation through fabrication-aware inverse design (FAID), integrating lithography models for deep-ultraviolet (DUV) lithography and electron beam lithography (EBL) into the shape optimization approach of ID. A Y-branch and an SWG-to-strip converter were generated and fabricated with this new approach. Simulated and measured results verify that the FAID yields devices with up to 0.6 dB lower insertion loss per device. The modified workflow enables designers to use ID to generate devices that adjust for process bias predicted by lithography models.

eess.SY

120 GOPS Photonic Tensor Core in Thin-film Lithium Niobate for Inference and in-situ Training

Photonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by enabling low-latency, high-speed, and energy-efficient computations. However, conventional photonic tensor cores face significant challenges in constructing large-scale photonic neuromorphic networks. Here, we propose a fully integrated photonic tensor core, consisting of only two thin-film lithium niobate (TFLN) modulators, a III-V laser, and a charge-integration photoreceiver. Despite its simple architecture, it is capable of implementing an entire layer of a neural network with a computational speed of 120 GOPS, while also allowing flexible adjustment of the number of inputs (fan-in) and outputs (fan-out). Our tensor core supports rapid in-situ training with a weight update speed of 60 GHz. Furthermore, it successfully classifies (supervised learning) and clusters (unsupervised learning) 112 * 112-pixel images through in-situ training. To enable in-situ training for clustering AI tasks, we offer a solution for performing multiplications between two negative numbers.

physics.optics