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Mohamad Hossein Idjadi

Publications and source records attributed to Mohamad Hossein Idjadi.

14 recordsLinked to original sources
arXiv2025

Chip-scale modulation-free laser stabilization using vacuum-gap micro-Fabry-Pérot cavity

Narrow-linewidth lasers are vital for a broad range of scientific and technological applications, including atomic clocks and precision sensing. Achieving high frequency stability is often as critical as ensuring scalability, portability, and cost-effectiveness in the development of low noise laser systems. Conventional electro-optic stabilization techniques, such as Pound-Drever-Hall locking to ultra-high-finesse resonators held in a vacuum chamber, provide excellent performance but remain challenging to scale. Here, we propose and experimentally demonstrate a cavity-coupled interferometric laser stabilization technique implemented on a silicon photonic chip and integrated with a compact, scalable micro-Fabry-Pérot cavity. The vacuum-gap optical cavity operates in air, achieving a quality factor of approximately $2.0\times 10^9$ and a fractional frequency instability of $1.45\times 10^{-12}$ at one-second averaging time. Integration of the proposed technique with the compact cavity yields more than 38-fold reduction in the laser's integrated linewidth and nearly three orders of magnitude suppression of frequency noise at 10 Hz offset frequency. The hybrid-integration of the proposed photonic chip with the micro-Fabry-Pérot cavity establishes a scalable and portable route toward chip-integrated ultra-stable lasers, paving the way for precision optical systems deployable beyond laboratory environments.

physics.optics↗
arXiv2025

Integrated photonic deep neural network with end-to-end on-chip backpropagation training

Integrated photonic neural networks (PNNs) have demonstrated significant potential to complement the digital electronic counterparts [1-3]. Nevertheless, robust and repeatable performance of scalable integrated PNNs is directly tied to the quality of their training. Error backpropagation (BP), which relies on nonlinear activation gradient computation, is the mainstream algorithm to train digital neural networks due to its scalability, versatility, and implementation efficiency [4]. Consequently, its adoption is highly desirable for the training of scalable PNNs. Despite such benefits and due to the lack of scalable on-chip activation gradient [5], PNNs have mostly been trained using a digital computer to run BP, which is inadequate in addressing device variations, or through gradient-free algorithms that do not fully benefit from the versatility of BP training. Here, we report the demonstration of an integrated photonic deep neural network with end-to-end on-chip gradient-descent BP training. All linear and nonlinear computations are performed on a single photonic chip, leading to scalable and robust training despite the considerable--but typical--fabrication-induced device variations. Two nonlinear data classification tasks are demonstrated in which the chip performance matches that of the ideal digital model, both in accuracy and robustness. Integrating the advantages of BP training with PNNs allows for generalization to various PNN architectures, paving the way for scalable and reliable next-generation photonic computing systems.

physics.optics↗
arXiv2024

Electronic Noise Considerations for Designing Integrated Solid-State Quantum Memories

As quantum networks expand and are deployed outside research laboratories, a need arises to design and integrate compact control electronics for each memory node. It is essential to understand the performance requirements for such systems, especially concerning tolerable levels of noise, since these specifications dramatically affect a system's design complexity and cost. Here, using an approach that can be easily generalized across quantum-hardware platforms, we present a case study based on nitrogen-vacancy (NV) centers in diamond. We model and experimentally verify the effects of phase noise and timing jitter in the control system in conjunction with the spin qubit's environmental noise. We further consider the impact of different phase noise characteristics on the fidelity of dynamical decoupling sequences. The results demonstrate a procedure to specify design requirements for integrated quantum control signal generators for solid-state spin qubits, depending on their coherence time, intrinsic noise spectrum, and required fidelity.

quant-ph↗
arXiv2024

Wideband, Efficient AlScN-Si Acousto-Optic Modulator in a Commercially Available Silicon Photonics Process

Acousto-optic integration offers numerous applications including low-loss microwave signal processing, nonreciprocal light propagation, frequency comb generation, and broadband acousto-optic modulation. State-of-the-art acousto-optic systems are mainly implemented entirely using in-house fabrication processes, which despite excellent performance typically suffer from low yield and are not compatible with mass production through foundry processes. Here, we demonstrate a highly efficient wideband acousto-optic modulator (AOM) implemented on a silicon photonics foundry process enabling high-yield low-cost mass production of AOMs with other photonic and electronic devices on the same substrate. In the reported structure, a 150 $μ$m long AlScN-based acoustic transducer launches surface acoustic waves (SAW), which modulate the light passing through a silicon optical waveguide. A modulation efficiency of -18.3 dB over a bandwidth of 112 MHz is achieved, which to our knowledge is the highest reported efficiency and bandwidth combination among silicon based AOMs, resulting in about an order of magnitude $BW(V_πL)^{-1}$ figure-of-merit improvement compared to the state-of-the-art CMOS compatible AOMs. The monolithically integrated acousto-optic platform developed in this work will pave the way for low-cost, miniature microwave filters, true time delays, frequency combs, and other signal processors with the advanced functionality offered by foundry-integrated photonic circuits.

physics.optics↗
arXiv2023

Dual-Polarization Phase Retrieval Receiver in Silicon Photonics

We demonstrate a silicon photonic dual-polarization phase retrieval receiver. The receiver recovers phase from intensity-only measurements without a local oscillator or transmitted carrier. We design silicon waveguides providing long delays and microring resonators with large dispersion to enable symbol-to-symbol interference and dispersive projection in the phase retrieval algorithm. We retrieve the full field of a polarization-division multiplexed 30-GBd QPSK and 20-GBd 8QAM signals over 80 km of SSMF.

physics.optics↗
arXiv2023

Silicon Photonic Direct-Detection Phase Retrieval Receiver

We demonstrate a direct-detection phase retrieval receiver based on silicon photonics. The receiver implements strong dispersion and delay lines on a compact chip. We retrieve the full field of a 30-GBd QPSK signal without a carrier or local oscillator.

physics.optics↗
arXiv2023

Frequency Tunable Magnetostatic Wave Filters With Zero Static Power Magnetic Biasing Circuitry

A single tunable filter simplifies complexity, reduces insertion loss, and minimizes size compared to frequency switchable filter banks commonly used for radio frequency (RF) band selection. Magnetostatic wave (MSW) filters stand out for their wide, continuous frequency tuning and high-quality factor. However, MSW filters employing electromagnets for tuning consume excessive power and space, unsuitable for consumer wireless applications. Here, we demonstrate miniature and high selectivity MSW tunable filters with zero static power consumption, occupying less than 2 cc. The center frequency is continuously tunable from 3.4 GHz to 11.1 GHz via current pulses of sub-millisecond duration applied to a small and nonvolatile magnetic bias assembly. This assembly is limited in the area over which it can achieve a large and uniform magnetic field, necessitating filters realized from small resonant cavities micromachined in thin films of Yttrium Iron Garnet. Filter insertion loss of 3.2 dB to 5.1 dB and out-of-band third order input intercept point greater than 41 dBm are achieved. The filter's broad frequency range, compact size, low insertion loss, high out-of-band linearity, and zero static power consumption are essential for protecting RF transceivers and antennas from interference, thus facilitating their use in mobile applications like IoT and 6G networks.

physics.app-ph↗
arXiv2023

Towards fully integrated photonic backpropagation training and inference using on-chip nonlinear activation and gradient functions

Gradient descent-based backpropagation training is widely used in many neural network systems. However, photonic implementation of such method is not straightforward mainly since having both the nonlinear activation function and its gradient using standard integrated photonic components is challenging. Here, we demonstrate the realization of two commonly used neural nonlinear activation functions and their gradients on a silicon photonic platform. Our method leverages the nonlinear electro-optic response of a micro-disk modulator. As a proof of concept, the experimental results are incorporated into a neural network simulation platform to classify MNIST handwritten digits dataset where we classification accuracies of more than 97\% are achieved that are on par with those of ideal nonlinearities and gradients.

cs.ET↗
arXiv2023

Modulation-free Laser Stabilization Technique Using Integrated Cavity-Coupled Mach-Zehnder Interferometer

Stable narrow-linewidth light sources play a significant role in many precision optical systems. Electro-optic laser frequency stabilization systems, such as the well-known Pound-Drever-Hall (PDH) technique, have been key components of stable laser systems for decades. These control loops utilize an optical frequency noise discriminator (OFND) to measure frequency noise and convert it to an electronic servo signal. Despite their excellent performance, there has been a trade-off between complexity, scalability, power consumption, and noise measurement sensitivity. Here, we propose and experimentally demonstrate a modulation-free laser stabilization technique using an integrated cavity-coupled Mach-Zehnder interferometer (MZI) as an OFND. The proposed architecture maintains the sensitivity and performance of the PDH architecture without the need for any modulation. This significantly improves overall power consumption, simplifies the architecture, and makes it easier to miniaturize into an integrated photonic platform. An on-chip microring resonator with a loaded quality factor of 2.5 million is used as the frequency reference. The implemented chip suppresses the frequency noise of a semiconductor laser by 4 orders of magnitude. The integral linewidth of the free-running laser is suppressed from 6.1 MHz to 695 KHz. The passive implemented photonic integrated circuit occupies an area of 0.456 mm$^2$ and is integrated on AIM Photonics 180 nm silicon-on-insulator process.

physics.optics↗
arXiv2023

Enhanced Frequency Noise Discrimination Using Cavity-coupled Mach-Zehnder Interferometer

Here, an enhanced frequency noise discriminator is proposed using a cavity-coupled Mach-Zehnder interferometer and demonstrated on a general-purpose programmable integrated photonics platform. The frequency noise measurement sensitivity is similar to the well-known Pound-Drever-Hall architecture but owns a passive, simpler, and more compact design. The proposed structure can be utilized in integrated laser frequency stabilization architectures.

physics.optics↗
arXiv2023

A surface-normal photodetector as nonlinear activation function in diffractive optical neural networks

Optical neural networks (ONNs) enable high speed parallel and energy efficient processing compared to conventional digital electronic counterparts. However, realizing large scale systems is an open problem. Among various integrated and non-integrated ONNs, free-space diffractive ONNs benefit from a large number of pixels of spatial light modulators to realize millions of neurons. However, a significant fraction of computation time and energy is consumed by the nonlinear activation function that is typically implemented using a camera sensor. Here, we propose a novel surface-normal photodetector (SNPD) with a nonlinear response to replace the camera sensor that enables about three orders of magnitude faster (5.7 us response time) and more energy efficient (less than 10 nW/pixel) response. Direct efficient vertical optical coupling, polarization insensitivity, inherent nonlinearity with no control electronics, low optical power requirements, and the possibility of implementing large scale arrays make the SNPD a promising nonlinear activation function for diffractive ONNs. To show the applicability, successful classification simulation of MNIST and Fashion MNIST datasets using the measured response of SNPD with accuracy comparable to that of an ideal ReLU function are demonstrated.

physics.optics↗