Sustainability

All optical tunable RF filter using elemental antimony

This work demonstrates the implementation of an integrated microwave photonic (MWP) processor unit using the transient crystallization dynamics of antimony (Sb) thin film. Our approach enables a significant reduction in device footprint compared to other integrated MWP processors, along with CMOS-compatible fabrication, enhancing scalability and reducing the overall energy budget. Also, the use of a low power femtosecond pulse to switch the Sb thin film can contribute to achieving energy-efficient system. The material used in this work is silicon, silicon oxide, and phase-change material Sb.

Spatio-spectral control of coherent nanophotonics

This work demonstrates the compact, integrated functionality to independently address multi-wavelength signals in a single photonic carrier, significantly reducing overall device footprints. As the system scales up, the decreased number of photonic components required for modulation channels, can ultimately improve any optical loss and reduce the overall energy required for postprocessing. However, this work primarily focuses on device footprint reduction, rather than energy efficiency, and thus future research specifically looking into sustainability and energy efficiency will be required

Higher-dimensional processing using a photonic tensor core with continuous-time data

This work demonstrates higher-dimensional processing by harnessing the RF degree of freedom coupled with spatial and wavelength degree of freedom. This approach enables ultrahigh parallelism in photonic computing, magnifying its advantages in terms of throughputs, energy efficiency, and compute density. The materials used are silicon, silicon dioxide, gold, and phase change material GST. At this point, we have not done any analysis on sustainability, recyclability, or even the energy efficiency versus net benefit analysis, and these should for part of any larger sustainability analysis.

Reduced rank photonic computing accelerator

Computing for AI tasks uses tremendous amounts of energy due to inefficient CMOS architecture. Photonic computing has been demonstrated for high throughput energy efficient computing. By proposing a network with smaller number of training parameters while maintaining the computational accuracy, this work improves the efficiency of such systems, meaning a smaller number of I/O controllers are required to set weights of a neural network and thus, scales favourably in terms of energy. The material used in this work is silicon, silicon oxide and phase change material AIST.

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

This paper is a large-scale review of memristive materials, devices and systems. As such it looks into emerging and published work in this field, and reviews the current state of the art. Some of the issues discussed include materials as well as the energy requirements of devices, in addition to their functionality, particularly in computing systems. However, the key issues related to sustainability are not the focus of this work, and thus, any future work aiming to look specifically into the sustainability aspects of this area will need a greater depth of research and understanding.

Integrated optical memristors

This paper is a review of devices that function as optical memristors, devices that serve in optics the same function as memristors do in electronics. As such the paper reviews the field broadly, but does make mention to energy requirements for each technology. The considerations for materials-related sustainabaility are not considered in detail in this paper. The energy requirements will also require further investigation, as those calculations are heavily dependent on the final application and use case.

In-memory photonic dot-product engine with electrically programmable weight banks

Computing in the emerging artificial intelligence space requires more efficient accelerators. This work progresses this area by showing the potential to combine electrical programming and optical probing to efficiently use optical computing alongside electronic computing for such applications. This work also reports a lower power consumption than previous implementations, having an energy plus. The materials used are standard silicon, and some phase change materials are used - the long term sustainability of these needs to be taken into account in any sustainability assessments.

Scalable High-Precision Trimming of Photonic Resonances by Polymer Exposure to Energetic Beams

Our paper on polymer trimming of photonic ring resonators describes a new technique to permanently modify the modal characteristics of photonic waveguides. The work takes advantage of mature fabrication technologies including electron beam lithography and photolithography to locally expose a commercial photosensitive PMMA. This approach can bring photonic components to specification thereby mitigating the energy expenditure required to transiently modify the effective index of the waveguides. The materials used are silicon, silicon nitride, silicon oxide and Poly(methyl methacrylate).

Atomically thin optomemristive feedback neurons

The group’s been pursuing new areas of computing to create more efficient computing paradigms for emerging artificial intelligence hardware. This work is one step to explore 2D materials in a new optoelectronic platform. Any implementation has to take into account life cycle of the new materials used as well as the overall system energy requirements in future.