The Electro-Optical Communication Systems (ECO) group, in collaboration with the Photonic Integration Group (PhI) and within the Department of Electrical Engineering of Eindhoven University of Technology (TU/e), is seeking to hire an outstanding PhD candidate on 'Hybrid-Photonic Neural Networks on Chip'.

We are seeking a highly skilled and motivated candidate to investigate and implement conventional and novel concepts of neuromorphic computation to be translated on the InP membrane on Silicon (IMOS) platform, relying on the possibility to co-integrate lasers, modulators and PDs and still reach ultra-compact form-factor and interfacing to ultra-fast electronics. Concepts of the in-memory computing will also be investigated and developed for this same platform and for the first time. This PhD research will enable mapping neural network models on monolithically integrated ultra-fast and ultra-compact chips, leading to highly efficient and parallel processing ultra-compact engines.

Research challenges

You will be working within the newly granted national NWA ORC (Research along Routes by Consortia) 'NL-ECO' project. Within this extensive project framework, we will focus on combining mapping photonics neural networks on IMOS technology for frontier neural network research and accelerators. The project is among the first to bridge electronic ICs and photonic ICs with the target of enable scalable, high bandwidth and low latency computation monolithically on chip. This research will enable a whole new class of reconfigurable neural network layouts, which could lead to highly efficient optical processing engines.

Several on-chip computing paradigms have already been proposed using light. However, optimal solutions and scalability at the small energy and low latency are still far ahead. A different approach needs to be explored and realized within this program. The Photonic Neural Network (PNN) Lab within the ECO group at TU/e aims to synergically collaborate with the Photonic Integration (PhI) group to leverage the intrinsic strengths of the IMOS photonic integration technology.

The PhD student will explore different brain-inspired neural network models and investigate suitability and mapping into programmable layout designs on chip, exploiting multi-project wafer and dedicated runs on IMOS, to include laser sources, modulators, PDs and all devices needed for enabling neural network based computation. He/she will explore localized (PCM based- or similar) memory for zero-power synaptic operations and of non-linearities on IMOS for the most energy efficient computation and footprint efficiency. He will develop proof-of-concept prototypes supporting high connectivity synaptic operations and photonic neural networks. He will perform scalability analysis and investigation of the maximum allowed neuron fan-in and its cascadability for deducing the physical layer metrics and foresee the ultimate computing metrics in terms of energy efficiency, computational speed and form-factor.

The team

The PhD position is based within the Electro-Optical Communication Systems (ECO) group, in collaboration with the Photonic Integration (PhI) group, which are both part of the Eindhoven Hendrik Casimir Institute (EHCI) and of the Eindhoven AI System Institute (EAISI). The ECO group has about 73 members, 55 of which are PhD students and PDs, and 18 are staff members, focusing on photonic technology for communication and computation. The PhI group focuses on ground-breaking designs and fabrication of top-notch integration technologies.

The EHCI has five dynamic and ambitious research groups, which are closely cooperating: a systems group, a photonic integration technology group and three materials research groups, with a predominant focus on neuromorphic and quantum computing is the ideal ecosystem where to develop this research. The EAISI brings together all AI activities of the TU/e. Top researchers from various departments and research groups work together to create new and exciting AI applications directly impacting the real world in collaboration with representatives from the industry.

The PhD student will also closely be connected to the NL-ECO consortium for idea cross-fertilization and for developing novel synergic research within the same consortium. The student will also closely interact with another PhD students who will do adjacent and connected work on the algorithms side.