Low Power Image Sensor for Distributed Processing in Cameras Network
Working in a collaborative academic project, your task will be to develop a smart image sensor for a wireless camera network embedding distributed AI computing.
Current camera network contains several standard cameras that transmit their images to a global server performing the targeted inference processing. This kind of architecture proposes energy and frugality performances that are not compatible with IoT requirements.
The project goal is to tackle hardware frugality through a distributed and collaborative approach based on ultra-low-power computing nodes. Each node’s inference core will be built around ASIC processors performing calculations in analog form. The final demonstrator will consist of a wireless network of “motes” (sensor network nodes) integrating dedicated image sensors paired with hybrid processors performing analog processing.
In this context, the mote’s image sensor must extract strategic features with frugality and efficiency which implies that you have to define, design and test an innovative readout architecture of a standard imager. In collaboration with the academic partners, you will be involved in the definition of the overall mote architecture allowing to define basically the output data format and the output procedure of the imager including potential pre-processing for the distributed inference computations. The studied architecture will integrate innovative low power solutions to address the targeted IoT applications and perform both image acquisitions and AI pre-processing.
As an image sensor demonstrator is planned in this PhD Thesis, the work will be conducted at CEA-Leti in the L3i Laboratory, using professional IC design tools and software development environments.
Study of new photodiode architecture for IR imagers
In the field of high-performance infrared detection, CEA-LETI plays a leading role in the development of the HgCdTe material, which today offers such performance that it is integrated into the James Webb Space Telescope (JWST) and allows the observation and study of deep space with unparalleled precision to date. However, we believe that it is still possible to make a significant step forward in terms of detection performance. Indeed, it seems that a fully depleted structure, called a PiN photodiode, could further reduce the dark current (and thus reduce noise and gain sensitivity at low photonic flux) compared to the non-fully depleted structures currently used. This architecture would represent the ultimate photodiode and would allow either a further increase in performance at a given operating temperature or a significant increase in the operating temperature of the detector, with the potential to open new fields of application by greatly simplifying cryogenics.
Your role in this thesis work will be to contribute to the development of the ultimate photodiode for very high-performance infrared detection, characterize and simulate the PiN photodiodes in HgCdTe technology manufactured on our photonic platform.
Candidate Profile:
You hold a Master's degree in optoelectronics and/or semiconductor material physics and are passionate about applied research.
The main technical skills required are: semiconductor component physics, optoelectronics, data processing, numerical simulations, interest in experimental work to carry out characterizations in a cryogenic environment but also theoretical work to carry out numerical simulations.
The PhD student will be integrated into a multidisciplinary team ranging from the growth of II-VI materials to electro-optical characterization, including microelectronics manufacturing processes in clean rooms and the packaging issues of such objects operating at low temperature.