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Photonic Chip Cuts Cost of Hunting Exoplanets



At 6.5 meters in diameter, the James Webb Space Telescope’s primary mirror captures more light than any telescope that’s ever been launched from Earth. But not every astronomer has US $10 billion to spend on a space telescope. So to help bring the cost of space-based astronomy down, researchers at the National Research Council of Canada in Ottawa are working on a way to process starlight on a tiny optical chip. Ross Cheriton, a photonics researcher there, and his students built and tested a CubeSat prototype with a new kind of photonic chip. The goal is to lower the barrier to entry for astronomical science using swarms of lower-cost spacecraft.

“We hope to enable smaller space telescopes to do big science using highly compact instrument-on-chips,” Cheriton says, who is also affiliated with the Quantum and Nanotechnology Research Centre in Ottawa.

Photonics integrated circuits (PICs) use light instead of electricity to process information, and they’re in wide use slinging trillions and trillions of bits around data centers. But only recently have astronomers begun to examine how to use them to push the boundaries of what can be learned about the universe.

Ground-based telescopes are plagued by Earth’s atmosphere, where turbulence blurs incoming light, making it difficult to focus it onto a camera chip. In outer space, telescopes can peer at extremely faint objects in non-visible wavelengths without correcting for the impact of turbulence. That’s where Cheriton aims to boldly go with a PIC filter that detects very subtle gas signatures during an exoplanet “eclipse” called a transit.

The main motivation for putting photonic chips in space is to reduce the size, weight, and cost of components, because it can be produced en masse in a semiconductor foundry. “The dream is a purely fiber and chip-based instrument with no other optics,” says Cheriton. Replacing filters, lenses, and mirrors with a chip also improves stability and scalability compared to ordinary optical parts.

CubeSats—inexpensive, small, and standardized satellites—have proved to be a cost-effective way of deploying small instrument payloads. “The compact nature of PICs is a perfect match for CubeSats to study bright exoplanet systems James Webb doesn’t have time to stare at,” says Cheriton.

For a total mission cost of less than $1 million—compared to the Webb’s $10 billion—an eventual CubeSat mission could stare at a star for days to weeks while it waits for a planet to cross the field of view. Then, it would look for slight changes in the star’s spectrum that are associated with how the planet’s atmosphere absorbs light—telltale evidence of gasses of a biological origin.

Smaller spectroscopy

As a proof-of-concept, Cheriton guided a team of undergraduate students who spent eight months designing and integrating a PIC into a custom 3U CubeSat (10 centimeter x 10 cm x 30 cm) platform. Their silicon nitride photonic circuit sensor proved itself capable of detecting the absorption signatures of CO2 in incoming light.

In their design, light entering the CubeSat’s collimating lens gets focused into a fiber and then pushed to the photonic chip. It enters an etched set of waveguides that includes a ring resonator. Here, light having a specific set of wavelengths builds in intensity over multiple trips around the ring, and is then output to a detector. Because only a select few wavelengths constructively interfere—those chosen to match a gas’s absorption spectrum—the ring serves as a comb-like filter. After the light goes through the ring resonator, the signal from the waveguide gets passed to an output fiber and onto a camera connected to a Raspberry Pi computer for processing. A single pixel’s intensity therefore serves as a reading for a gas’s presence.

red light with small black boxes Light travels through a waveguide on a photonic integrated circuit.Teseract

Because it’s built on a chip, the sensor could be multiplexed for observing several objects or sense different gasses simultaneously. Additionally, all the light falling on a single pixel means that the signal is more sensitive than a traditional spectrometer, says Cheriton. Moreover, instead of hunting for peaks in a full spectrum, the technology looks for how well the absorption spectrum matches that of a specific gas, a more efficient process. “If something is in space, you don’t want to send gigabytes of data home if you don’t have to,” he says.

Space travel is still a long way off for the astrophotonic CubeSat. The current design does not use space-qualified components. But Cheriton’s students tested it in the lab for red light (635 nm) and CO2 in a gas cell. They used a “ground station” computer to transmit all commands and receive all results—and to monitor the photovoltaics and collect data from the flight control sensors onboard their CubeSat.

Next, the team plans to test whether their sensor can detect oxygen with the silicon nitride chip, a material that was chosen for its transparency to the gas’s 760 nm wavelength. Success would leave them well positioned to meet what Cheriton calls the next huge milestone for astronomers: looking for an earth-like planet with oxygen.

The work was presented at the Optica (formerly Optical Society of America) Advanced Photonics conference in July.

Dedicated Approximate Computing Framework To Efficiently Compute PCs On Hardware

A technical paper titled “On Hardware-efficient Inference in Probabilistic Circuits” was published by researchers at Aalto University and UCLouvain.

Abstract:

“Probabilistic circuits (PCs) offer a promising avenue to perform embedded reasoning under uncertainty. They support efficient and exact computation of various probabilistic inference tasks by design. Hence, hardware-efficient computation of PCs is highly interesting for edge computing applications. As computations in PCs are based on arithmetic with probability values, they are typically performed in the log domain to avoid underflow. Unfortunately, performing the log operation on hardware is costly. Hence, prior work has focused on computations in the linear domain, resulting in high resolution and energy requirements. This work proposes the first dedicated approximate computing framework for PCs that allows for low-resolution logarithm computations. We leverage Addition As Int, resulting in linear PC computation with simple hardware elements. Further, we provide a theoretical approximation error analysis and present an error compensation mechanism. Empirically, our method obtains up to 357x and 649x energy reduction on custom hardware for evidence and MAP queries respectively with little or no computational error.”

Find the technical paper here. Published May 2024 (preprint). CODE: https://github.com/lingyunyao/AAI_Probabilistic_Circuits

Yao, Lingyun, Martin Trapp, Jelin Leslin, Gaurav Singh, Peng Zhang, Karthekeyan Periasamy, and Martin Andraud. “On Hardware-efficient Inference in Probabilistic Circuits.” arXiv preprint arXiv:2405.13639 (2024).

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The post Dedicated Approximate Computing Framework To Efficiently Compute PCs On Hardware appeared first on Semiconductor Engineering.

Chip Industry Technical Paper Roundup: May 13

New technical papers added to Semiconductor Engineering’s library this week.

Technical Paper Research Organizations
Cross-layer Modeling and Design of Content Addressable Memories in Advanced Technology Nodes for Similarity Search Georgia Tech
An ultra energy-efficient hardware platform for neuromorphic computing enabled by 2D-TMD tunnel-FETs University of California Santa Barbara
Efficient Approaches for GEMM Acceleration on Leading AI-Optimized FPGAs University of Texas at Austin and Arizona State University
Explaining EDA synthesis errors with LLMs University of New South Wales and University of Calgary
Materials for High Temperature Digital Electronics University of Pennsylvania, Air Force Research Laboratory, and Ozark Integrated Circuits
Synthesis of goldene comprising single-atom layer gold Linköping University
Thermal Crosstalk Modelling and Compensation Methods for Programmable Photonic Integrated Circuits Technical University of Denmark and iPronics Programmable Photonics

More Reading
Technical Paper Library home

The post Chip Industry Technical Paper Roundup: May 13 appeared first on Semiconductor Engineering.

Framework For Early Anomaly Detection In AMS Components Of Automotive SoCs

A technical paper titled “Enhancing Functional Safety in Automotive AMS Circuits through Unsupervised Machine Learning” was published by researchers at University of Texas at Dallas, Intel Corporation, NXP Semiconductors, and Texas Instruments.

Abstract:

“Given the widespread use of safety-critical applications in the automotive field, it is crucial to ensure the Functional Safety (FuSa) of circuits and components within automotive systems. The Analog and Mixed-Signal (AMS) circuits prevalent in these systems are more vulnerable to faults induced by parametric perturbations, noise, environmental stress, and other factors, in comparison to their digital counterparts. However, their continuous signal characteristics present an opportunity for early anomaly detection, enabling the implementation of safety mechanisms to prevent system failure. To address this need, we propose a novel framework based on unsupervised machine learning for early anomaly detection in AMS circuits. The proposed approach involves injecting anomalies at various circuit locations and individual components to create a diverse and comprehensive anomaly dataset, followed by the extraction of features from the observed circuit signals. Subsequently, we employ clustering algorithms to facilitate anomaly detection. Finally, we propose a time series framework to enhance and expedite anomaly detection performance. Our approach encompasses a systematic analysis of anomaly abstraction at multiple levels pertaining to the automotive domain, from hardware- to block-level, where anomalies are injected to create diverse fault scenarios. By monitoring the system behavior under these anomalous conditions, we capture the propagation of anomalies and their effects at different abstraction levels, thereby potentially paving the way for the implementation of reliable safety mechanisms to ensure the FuSa of automotive SoCs. Our experimental findings indicate that our approach achieves 100% anomaly detection accuracy and significantly optimizes the associated latency by 5X, underscoring the effectiveness of our devised solution.”

Find the technical paper here. Published April 2024 (preprint).

Arunachalam, Ayush, Ian Kintz, Suvadeep Banerjee, Arnab Raha, Xiankun Jin, Fei Su, Viswanathan Pillai Prasanth, Rubin A. Parekhji, Suriyaprakash Natarajan, and Kanad Basu. “Enhancing Functional Safety in Automotive AMS Circuits through Unsupervised Machine Learning.” arXiv preprint arXiv:2404.01632 (2024).

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The post Framework For Early Anomaly Detection In AMS Components Of Automotive SoCs appeared first on Semiconductor Engineering.

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