Funding type: COFUND Marie Slodowska Curie Action

This project is under the Marie Skłodowska-Curie Actions (MSCA) program. There are no nationality conditions but the candidates must fulfill the MSCA mobility conditions, which means that she/he must not have stayed more than 1 year in France during the last 3 years immediately before the call deadline (31/05/2019).

Expected start date: 01/10/2020

Contacts:

Dr. Fabio Pavanello 
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Pr. Ian O’Connor, INL
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Dr. Andreas Boes
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Pr. Arnan Mitchell, RMIT
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Websites:

• https://www.rmit.edu.au/about/our-locations-and-facilities/facilities/research-facilities/micronano-research-facility
• http://inl.cnrs.fr/en/

Collaborations/External partners :

Domain and scientific context :

Recently, the exponential demand for computing systems addressing the I/O bottleneck i.e. providing higher communication bandwidths and lower power consumption has fostered a significant amount of work into “More than Moore” approaches. Particularly, analog computing approaches based on stochastic and neuromorphic computing are expected to provide considerable benefits over more traditional transistor-based von Neumann approaches in e.g. machine learning (ML)-based applications thanks to their highly parallel architectures, suitability to implement cost-effectively matrix multiplication operations and to the possibility of exploiting a whole range of emerging technologies e.g. non-volatile memory (NVM) components, potentially leading to better energy consumption and bandwidth performance. More specifically, integrated photonic approaches in Von Neumann architectures are already emerging as a viable solution to solve such bottleneck targeting, at first, high performance and data-centric computing applications [1]–[3]. In this context, the advantages of integrated photonics are currently being explored also for stochastic and neuromorphic computing. However, key photonic devices exploiting e.g. memory effects for such applications have several limitations (e.g. short cavity lifetime, large footprint) in standard Silicon-On-Insulator (SOI) platforms for telecom applications. Hence, the scientific interest of exploring other platforms such as the Lithium Niobate on Insulator (LNOI) platform which provides all the standard photonic components (waveguides, modulators, filters…) as well as photonic memory-dependent devices without the limitations of SOI platforms thus leading to potentially higher performance computing systems.

Keywords : Lithium Niobate, electro-optic effects, non-volatile memory, nanophotonics, integrated optics, neuromorphic computing

Objectives and scientific challenges:

Although SOI platforms have been one of the main candidates to develop transceivers (using light as a mean of data transfer), there is much less consensus on the ideal platform for optical analog computing. One of the key disadvantages of SOI is power budget, as the weights for optical ananlog computing on PICs are adjusted by local heating of the waveguides, and this requires surprisingly large amounts of energy to tune the weights. For example, a phase weight may require 0.25 watts to give a typical phase shift of π [4]. A combined amplitude and phase weight requires double this power. On top of this is power/space required for cooling. Thus, energy for tuning is the major bottleneck for the adoption of PICs in miniaturized optical systems. In fact, notwithstanding SOI platforms have been exploited for demonstrations of optical neuromorphic computing systems, they lack of compact, robust and long lifetime memory-dependent photonic devices [5], [6].

LNOI has emerged as a promising platform allowing low loss propagation, low energy electro-static switching and the potential for dense integration with detectors and sources hybrid integrated onto a single chip platform using similar technologies to those already established for SOI integrated photonic chips. [7]. RMIT University has a campaign of research activities to build modular building blocks on the LNOI platform including modulators, switches, resonators and also hybrid integrated detectors and sources as well as the technologies to package and interface these platforms so that they can be interfaced to dense electronic control such as FPGA processing elements. A particular emphasis of this research is to create programmable and reconfigurable optical circuits with low power requirements to enable future information processing systems.

The objectives of this PhD study will be (1) to identify the system requirements for neuromorphic computing on a photonic chip harnessing the existing building blocks and identifying any missing building blocks or bottlenecks that could be the focus of further research; (2) work with the existing teams at RMIT to design and realise a suite of demonstrator circuits and characterize these as record breaking neuromorphic computing elements; (3) explore the opportunity for new photonic chip elements harnessing reversable domain inversion on lithium niobate as a means of non-volatile photonic chip reconfiguration. In particular, this platform could provide a considerable improvement in terms of power consumption over other systems because of the very low switching power required for such memory-dependent components.

Expected original contributions :

• Pioneering design and demonstration of photonic chip platforms for neuromorphic computing applications using the LNOI platform
• Exploration of a new non-volatile programmable circuit element based on reverable domain inversion on lithium niobate
• Advancement of building blocks to increase scalability, switching/writing speed and power consumption
• Creation of some of the first photonic chip architectures leveraging these novel building blocks and experimental demonstration of initial prototypes.

Research program and methodology :

The PhD student will be involved in all the different research aspects concerning the design, fabrication and characterization of building blocks and initial prototypes. In particular, S/he will carry out design work of photonic system architectures at INL.  The student will first be trained in using the integrated photonic design framework (IPKISS by Luceda Photonics) used by RMIT University and enhanced to accommodate LNOI and using both commercial and custom system and device level tools.  In particular, system designs will harness an event-based simulator to design architectures has been recently developed at INL for stochastic computing using phase-change materials, which could be adapted to the LNOI platform and the IPKISS integrated REME which is uniquely capable of designing devices in the LNOI platform. While at INL, the student will design a number of simple circuits and will coordinate with the RMIT team in Australia to have these circuits realized and delivered to INL for characterization using an electro-optic test setup

The second year will be spent at RMIT learning how designs are converted into practical chips with the opportunity to learn fabrication and packaging skills. They will particularly use this time to explore enhanced circuit elements including non-volatile programmable elements that can be used for more sophisticated information processing circuits.

In the final year, the student will return to INL with the second generation of photonic circuits and will conduct record breaking demonstrations with these chips.  The final year will present the opportunity for an third iteration, pushing the boundaries of system complexity realizing the vision of programmable system design initiated in the first year and incorporating the insights into practicalities and opportunities of device and system realization gained in the second year.  This will enable a final breakthrough demonstration that will complete the PhD.

The student will be working with the Nanophotonics research group at INL hosted by Ecole Centrale de Lyon and the Integrated Photonics and Applications Centre (InPAC) by Prof. Arnan Mitchell at RMIT for integrated photonic devices. The student will benefit from INL's and RMIT’s resources and photonics expertise, both in terms of device/system design and on technology and clean-room manufacturing aspects for the production of the first basic prototypes. S/he will join a team of pioneers in the field of integrated photonics and emerging technologies for computing applications.

Collaborations with the Photonics Research Group at Ghent University (Prof. Peter Bienstman) will be established within the PhD topic.

Tentative timeline for the PhD studies:

Year 1: Based at INL - learning design framework and initial design of programmable neuromorphic computing circuits harnessing existing building blocks.  Application scenarios will be first considered from the theoretical standpoint through advanced system-level behavioural modelling at THALES. Enhancement of existing simulators for stochastic computing already prepared at INL for use with phase change materials. Coordination with team at RMIT to fabricate these initial designs. Characterization of circuits and components from RMIT and initial demonstration of neuromorphic computing circuits at INL with the involvement of Thales. . Assessment of missing building blocks and bottleneck components that are the key to scalability, programming/processing speed and power consumptions.

Year 2: Based at RMIT University - opportunity to learn clean-room fabrication and packaging and interfacing of photonic chips at RMIT so that the chips are of sufficient complexity and can be practically interfaced to FPGA electronic control.  Focus on enhancing bottle neck components to enable more sophisticated circuits.  Explore novel programmable elements based on domain inversion. Work with team to realise second iteration system harnessing improved components.

Year 3: Based at INL – Demonstration of second iteration system including electronic control with FPGA . Assessment of viability of improved components to enable improved neuromorphic functionality.  Define architecture for third iteration design (optional, depending on progress). Assessment system reliability under temperature fluctuations. Writing of the thesis.

Scientific supervision:

• Description of the supervision committee :

• Integration inside the laboratories (percentage of working time inside these laboratories) : 67% at INL, 33% at RMIT

PhD funding : Co-Fund Marie Sladowska Curie Action (MSCA) ECL/RMIT (ECLAUsion program)

Profile of the candidate :

We seek a talented and ambitious researcher with a good knowledge and a solid background in the field of solid-state physics, optics, and semiconductor devices. S/he should work towards his/her Masters/honours or Engineering degree in a field apposite to one of these areas. An experience in photonics, clean-room fabrication, programming or optical modeling and characterization will be strongly appreciated.

Objectives for the valorization of the research work:

The results obtained will be published in peer-reviewed journals with a high impact factor and presented at international conferences in the field (CLEO-US/Europe, SPIE photonics Europe…).

Skills that will be developed during the PhD :

The PhD work lies at the frontier between material science and nanophotonics (devices and systems). The student will thus develop skills related to these two highly complementary technological areas. The PhD student will thus gain experience in the "nanophotonics / nanotechnology" field, from the device to the system design (simulation and design of optical microcomponents, FDTD-Finite difference time domain, FEMSIM-finite element method, event-based system-level simulators), the nanofabrication of these devices and systems in clean-room environments (e-beam and optical lithography, dry and wet etching), and their optical and electro-optical characterization acquiring expertise in key optical fiber-based testing equipment (pulsed lasers, spectrum analyzers…), measurement automation and field programmable gate arrays (FPGAs). The highly collaborative and international environment of the project will require the student to develop, in addition to technical and scientific skills, communication, teamwork and project management skills.

Professional opportunities after the PhD:

The PhD student will be involved in the whole process (design, fabrication and characterization) of demonstrating how a well-established and application-relevant platform can be used in the field of analog computing, which is currently gaining strong attention by the scientific community because of its potential to drastically improve the computing performance of current I/O-limited systems. This will allow the student to become confortable with several topics of current relevance such as neuromorphic computing, high-speed systems, optical networks on-chip etc. which are expected to cover an even larger application scenario in the future thus providing exciting opportunities for the skilled person. Furthermore, the presence of Thales as industrial partner will provide the PhD student with a unique opportunity to gain insight into which industry-driven applications will be of utmost interest in the near future. Finally, the nanotechnology and computational tools used by the student during the PhD will be relevant for a large number of application fields and will allow him/her to find a job in the photonics or microelectronics industry. The future prospects for the student, at the end of his thesis, include the possibility of pursuing an academic career in a prestigious photonics laboratory or joining an industry in the microelectronics or photonics sector, especially given the industrial interactions expected during the project duration.

Bibliographic references about the PhD topic :

1. D. Miller, “Device requirements for optical interconnects to silicon chips,” Proc. IEEE, vol. 97, no. 7, pp. 1166–1185, Jul. 2009.
2. Nikolova et al., “Scaling silicon photonic switch fabrics for data center interconnection networks,” Opt. Expess, vol. 23, no. 2, pp. 1159–1175, 2015.
3. C. Sun et al., “Single-chip microprocessor that communicates directly using light,” Nature, vol. 528, no. 7583, pp. 534–538, 2015.
4. Y. Xie et al., “Programmable optical processor chips: Toward photonic RF filters with DSP-level flexibility and MHz-band selectivity,” Nanophotonics, vol. 7, no. 2, pp. 421–454, 2017.
5. A. Katumba, M. Freiberger, P. Bienstman, and J. Dambre, “A Multiple-Input Strategy to Efficient Integrated Photonic Reservoir Computing,” Cognit. Comput., vol. 9, no. 3, pp. 307–314, 2017.
6. D. Le Coarer et al., “All-Optical Reservoir Computing on a Photonic Chip Using Silicon-Based Ring Resonators,” IEEE J. Sel. Top. Quantum Electron., vol. 24, no. 6, pp. 1–8, 2018.
7. A. Boes, B. Corcoran, L. Chang, J. Bowers, and A. Mitchell, “Status and Potential of Lithium Niobate on Insulator (LNOI) for Photonic Integrated Circuits,” Laser Photonics Rev., vol. 12, no. 4, p. 1700256, Apr. 2018.