arXiv subjects

Akhil Varri

Publications and source records attributed to Akhil Varri.

5 recordsLinked to original sources

arXiv2026

Ion-Implanted Silicon Nanoregions Enable Ultra-Low-Loss Trimming of Cladded Photonic Integrated Circuits

Photonic integrated circuits (PICs) have emerged as a key platform for information processing, including optical communication and computing. As PIC complexity increases, fabrication-induced response deviations accumulate, making post-fabrication trimming critical for unlocking their full potential. Here, we demonstrate that silicon ion implantation enables scalable, ultra-low-loss trimming of cladded PICs by locally forming a high-index silicon-rich region. Structural characterization confirms that the silicon-implanted nanoregion is confined to the cladding without observable damage to the underlying waveguide. The implantation-induced excess loss is below 0.001 dB per {\pi} phase shift, while the optical response remains stable over a four-month observation period. Automated trimming is demonstrated on a photonic crossbar array, reducing the average channel output variation from 78.5% to 5.9%. These results establish a practical route towards automated, ultra-low-loss post-fabrication trimming for large-scale cladded PICs.

physics.optics↗

arXiv2026

Voltage-Programmable Photon Statistics Using a High-Extinction Thin-film Lithium Niobate Modulator

Controlling the statistical properties of light, namely the fluctuations in photon arrival, entropy and number, is essential for both classical and quantum photonics. While integrated systems provide tunable control over amplitude, phase, and wavelength, real-time modulation of photon statistics has remained a long-standing challenge. Herein, we introduce the concept and experimental realization of a photon statistics transducer: a high-extinction, broadband electro-optic device capable of deterministically shaping photon-number distributions at nanosecond timescales. Our approach employs a cascaded thin-film lithium niobate (TFLN) Mach-Zehnder amplitude modulator delivering more than 50 dB extinction, enabling precise suppression and release of coherent seed light from an integrated InP laser. By exploiting the interplay between seed suppression and erbium-doped fiber amplifier dynamics, we demonstrate smooth, voltage-controlled switching between Poissonian and super-Poissonian photon statistics, with second-order coherence g2(0) tunable from 1.0 to 1.7. Complementary measurements with superconducting nanowire single-photon detectors further show photon-flux control down to sub-photon levels, highlighting the potential for future operation with non-classical sources. The photon statistics transducer thus establishes statistical modulation as a new functional primitive in integrated photonics. Applications range from entropy generation and secure communication to neuromorphic and hybrid quantum-classical processing, where controlled randomness and entropy are essential resources. By enabling programmable transitions between statistical regimes using only electronic drive signals, our work lays the foundation for adaptive, entropy-aware photonic systems that bridge classical and quantum domains.

physics.optics↗

arXiv2026

Probabilistic Photonic Computing

Probabilistic computing excels in approximating combinatorial problems and modelling uncertainty. However, using conventional deterministic hardware for probabilistic models is challenging: (pseudo) random number generation introduces computational overhead and additional data shuffling, which is particularly detrimental for safety-critical applications requiring low latency such as autonomous driving. Therefore, there is a pressing need for innovative probabilistic computing architectures that achieve low latencies with reasonable energy consumption. Physical computing offers a promising solution, as these systems do not rely on an abstract deterministic representation of data but directly encode the information in physical quantities. Therefore, they can be seamlessly integrated with physical entropy sources, enabling inherent probabilistic architectures. Photonic computing is a prominent variant due to the large available bandwidth, several orthogonal degrees of freedom for data encoding and optimal properties for in-memory computing and parallel data transfer. Here, we highlight key developments in physical photonic computing and photonic random number generation. We provide insights into the realization of probabilistic photonic processors and lend our perspective on their impact on AI systems and future challenges.

physics.app-ph↗

arXiv2024

Roadmap to Neuromorphic Computing with Emerging Technologies

The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently utilized technologies, providing an overview of emerging technologies, addressing material challenges, exploring novel computing concepts, and finally examining the maturity level of emerging technologies while determining the next essential steps for their advancement.

eess.SP↗

arXiv2024

Probabilistic Photonic Computing with Chaotic Light

Biological neural networks effortlessly tackle complex computational problems and excel at predicting outcomes from noisy, incomplete data, a task that poses significant challenges to traditional processors. Artificial neural networks (ANNs), inspired by these biological counterparts, have emerged as powerful tools for deciphering intricate data patterns and making predictions. However, conventional ANNs can be viewed as "point estimates" that do not capture the uncertainty of prediction, which is an inherently probabilistic process. In contrast, treating an ANN as a probabilistic model derived via Bayesian inference poses significant challenges for conventional deterministic computing architectures. Here, we use chaotic light in combination with incoherent photonic data processing to enable high-speed probabilistic computation and uncertainty quantification. Since both the chaotic light source and the photonic crossbar support multiple independent computational wavelength channels, we sample from the output distributions in parallel at a sampling rate of 70.4 GS/s, limited only by the electronic interface. We exploit the photonic probabilistic architecture to simultaneously perform image classification and uncertainty prediction via a Bayesian neural network. Our prototype demonstrates the seamless cointegration of a physical entropy source and a computational architecture that enables ultrafast probabilistic computation by parallel sampling.

physics.optics↗