Design and integration of microlasers within a silicon photonics platform
For about ten years, the continuous increase in internet traffic has pushed the electrical interconnections of data centers to their limits in terms of bandwidth, density, and consumption. By replacing these electrical links with optical fibers and integrating all the necessary optical functions on a chip to create transmitters-receivers (transceivers), silicon photonics represents a unique opportunity to address these issues. The integration of a light source within a photonic chip is an essential building block for the development of this technology. While many demonstrations rely on the use of external lasers or bonded laser chips, it is the direct heterogeneous fabrication of a laser within the photonic chip that would allow the desired level of performance while limiting costs.
The objective of this thesis is to provide an innovative solution for the management of very short-distance communications (inter-chip, intra-chip) by realizing, on silicon, III-V membrane microlaserswith buried heterostructures. This type of microlasermeets the numerous challenges of very short-distance links thanks to an efficiency/integrabilitycompromise superior to the state of the art of datacomlasers while being compatible with CMOS fabrication lines.
Based on the work carried out during a previous thesis, the PhD student will be responsible for (i) designing the microlasersusing the available digital simulation tools in the laboratory, then (ii) manufacturing these microlasersby relying on the technological platforms of CEA-LETI, and finally (iii) electro-optically characterizing the components. This thesis work will be carried out in collaboration between CEA-LETI and LTM/CNRS and will constitute a strategic brick necessary for future generations of photonic transceivers.
Laser Fault Injection Applied to Reverse Engineering of Memories
Memories play a critical role for the security of cyber-physical systems. They manage sensitive data such as cryptographic keys and proprietary codes. With the increasing threat of hardware attacks, understanding and manipulating memory organization has become essential. The thesis aims to explore the application of laser attack techniques, specifically Thermal Laser Stimulation (TLS) and laser perturbation, to reverse engineer memory systems. The primary objective is to develop methods for extracting or modifying memory content, with a particular focus on validating TLS on FDSOI 22nm technology. Additionally, the thesis seeks to use laser perturbation for reconstructing memory architecture, analyzing error-correcting codes, and designing countermeasures. The research will leverage the infrastructures available at CEA (e.g.,https://github.com/CEA-Leti/secbench), as well as the expertise of the laboratory members.
Scalability of the Network Digital Twin in Complex Communication Networks
Communication networks are experiencing an exponential growth both in terms of deployment of network infrastructures (particularly observed in the gradual and sustained evolution towards 6G networks), but also in terms of machines, covering a wide range of devices ranging from Cloud servers to lightweight embedded IoT components (e.g. System on Chip: SoC), and including mobile terminals such as smartphones.
This ecosystem also encompasses a variety of software components ranging from applications (e.g. A/V streaming) to the protocols from different communication network layers. Furthermore, such an ecosystem is intrinsically dynamic because of the following features:
- Change in network topology: due, for example, to hardware/software failures, user mobility, operator network resource management policies, etc.
- Change in the usage/consumption ratio of network resources (bandwidth, memory, CPU, battery, etc.). This is due to user needs and operator network resource management policies, etc.
To ensure effective supervision or management, whether fine-grained or with an abstract view, of communication networks, various network management services/platforms, such as SNMP, CMIP, LWM2M, CoMI, SDN, have been proposed and documented in the networking literature and standard bodies. Furthermore, the adoption of such management platforms has seen broad acceptance and utilization within the network operators, service providers, and the industry, where the said management platforms often incorporate advanced features, including automated control loops (e.g. rule-based, expert-system-based, ML-based), further enhancing their capability to optimize the performance of the network management operations.
Despite the extensive exploration and exploitation of these network management platforms, they do not guarantee an effective (re)configuration without intrinsic risks/errors, which can cause serious outage to network applications and services. This is particularly true when the objective of the network (re)configuration is to ensure real-time optimization of the network, analysis/ tests in operational mode (what- if analysis), planning updates/modernizations/extensions of the communication network, etc. For such (re)configuration objectives, a new network management paradigm has to be designed.
In the recent years, the communication network research community started exploring the adoption of the digital twin concept for the networking context (Network Digital Twin: NDT). The objective behind this adoption is to help for the management of the communication network for various purposes, including those mentioned in the previous paragraph.
The NDT is a digital twin of the real/physical communication network (Physical Twin Network: PTN), making it possible to manipulate a digital copy of the real communication network, without risk. This allow in particular for visualizing/predicting the evolution (or the behavior, the state) of the real network, if this or that network configuration is to be applied. Beyond this aspect, the NDT and the PTN network exchange information via one or more communication interfaces with the aim of maintaining synchronized states between the NDT and the PTN.
Nonetheless, setting up a network digital twin (NDT) is not a simple task. Indeed, frequent and real-time PTN-NDT synchronization poses a scalability problem when dealing with complex networks, where each network information is likely to be reported at the NDT level (e.g. a very large number of network entities, very dynamic topologies, large volume of information per node/per network link).
Various scientific contributions have attempted to address the question of the network digital twin (NDT). The state-of-the-art contributions focus on establishing scenarios, requirements, and architecture for the NDT. Nevertheless, the literature does not tackle the scalability problem of the NDT.
The objective of this PhD thesis is to address the scalability problem of network digital twins by exploring new machine learning models for network information selection and prediction.
Automatization of quantum computing kernel writing for quantum applications
The framework of Hamiltonian simulation opens up a new range of computational approaches for quantum computing. These approaches can be developed across all relevant fields of quantum computing applications, including, among others, partial differential equations (electromagnetism, fluid mechanics, etc.), quantum machine learning, finance, and various methods for solving optimization problems (both heuristic and exact).
The goal of this thesis is to identify a framework where these approaches—based on Hamiltonian simulation or block-encoding techniques—are feasible and can be written in an automated way.
This work could extend to the prototyping of a code generator, which would be tested on practical cases in collaboration with European partners (including a few months of internship within their teams).
Increasing the electrothermal robustness of new SiC devices
Silicon Carbide (SiC) is a semiconductor with superior intrinsic properties than Silicon for high temperature and high power electronics applications. SiC devices are expected to be extensively used in the electrification transition and novel energy management applications. To fully exploit the SiC superior properties, the future semiconductor devices will be used under extreme biasing and temperature conditions. These devices must operate safely at higher current densities, higher dV/dt and higher junction temperatures than Si devices does.
The objective of this thesis is to study the SiC devices fabricated at LETI under these extreme operating conditions, and to optimize their design to fully use the theoretical potential of SiC. The thesis work will include several phases that will be strongly coupled:
- Advanced electro-thermal characterisation (50%), by proposing new approaches to testing components in a box or on a suitable support, using artificial intelligence (AI) tools for data extraction and processing. The work will include adapting standard measurement methodologies to the specific switching characteristics of SiC.
- An assessment (15%) of the design and technological parameters responsible for the operating limits of the components.
- A physico-chemical characterisation component (15%) to analyse failures under these extreme conditions.
- The inclusion of predictive models (20%) for the sensitivity of architectures to extreme conditions and faults, based on modelling.
Design and optimization of color routers for image sensors
Color routers represent a promising technology that could revolutionize the field of image sensors. Composed of nanometricstructures called metasurfaces, these devices allow the modification of light propagation to improve the quantum efficiency of pixels. Thanks to recent technical advances, it is now possible to design and manufacture these structures, paving the way for more efficient image sensors.
The thesis topic focuses on the design and optimization of color routers for image sensors. Several research avenues will be explored, such as the implementation of new metasurfacegeometries (`freeform`) or innovative configurations to reduce pixel pitch (0.5µm or 0.6µm). Various optimization methods can be used, such as the adjointmethod, machine learning, or the use of auto-differentiable solvers. The designs must be resilient to the angle of light incidence and expected variations during manufacturing. After this simulation phase, the proposed structures will be manufactured, and the student will have the mission to characterize the chips and analyze the obtained results (quantum efficiency, modulation transfer function...).
This thesis will be co-supervised by STMicroelectronics and CEA LETI in Grenoble. The student will be integrated into the teams of engineer-researchers working on this project. He/she will be led to collaborate with various specialists in various fields such as lithography and optical characterization.
The student's main activities:
- Optical simulation using numerical methods (FDTD, RCWA)
- Development of optimization methodologies for metasurfacedesign (adjointmethod, topological optimization...)
- Electro-optical characterization and analysis of experimental data
Sub-THz programmable electromagnetic surfaces based on phase change material switches
Spatiotemporal manipulation of the near- and far-electromagnetic (EM)-field distribution and its interaction with matter in the THz spectrum (0.1-0.6 THz) is of prime importance in the development of future communication, spectroscopy, imaging, holography, and sensing systems. Reconfigurable Intelligent (Meta)Surface (RIS) is a cutting-edge hybrid analogue/digital architecture capable of shaping and controlling the THz waves at the subwavelength scale. To democratize the RIS technology, it will be crucial to reduce its energy consumption by two orders of magnitude. However, the state-of-the-art does not address the integration, scalability, wideband and high-efficiency requirements.
Based on our recent research results, the main objective of this project will be to demonstrate novel silicon-based RIS architectures s at 140 GHz and 300 GHz. The enhancement of the THz RIS performance will derive from a careful choice of the silicon technology and, from novel wideband meta-atom designs (also called unit cell or element) with integrated switches based on PCM (phase change material). The possibility of dynamically controlling the amplitude of the transmission coefficients of the meta-atoms, besides their phase, will be also investigated. Near-field illumination will be introduced to obtain an ultra-low profile. To the best of our knowledge, this constitutes a new approach for the design of high-gain antennas in the sub-THz range.
Defense of scene analysis models against adversarial attacks
In many applications, scene analysis modules such as object detection and recognition, or pose recognition, are required. Deep neural networks are nowadays among the most efficient models to perform a large number of vision tasks, sometimes simultaneously in case of multitask learning. However, it has been shown that they are vulnerable to adversarial attacks: Indeed, it is possible to add to the input data some perturbations imperceptible by the human eye which undermine the results during the inference made by the neural network. However, a guarantee of reliable results is essential for applications such as autonomous vehicles or person search for video surveillance, where security is critical. Different types of adversarial attacks and defenses have been proposed, most often for the classification problem (of images, in particular). Some works have addressed the attack of embedding optimized by metric learning, especially used for open-set tasks such as object re-identification, facial recognition or image retrieval by content. The types of attacks have multiplied: some universal, other optimized on a particular instance. The proposed defenses must deal with new threats without sacrificing too much of the initial performance of the model. Protecting input data from adversarial attacks is essential for decision systems where security vulnerabilities are critical. One way to protect this data is to develop defenses against these attacks. Therefore, the objective will be to study and propose different attacks and defenses applicable to scene analysis modules, especially those for object detection and object instance search in images.
RF Circuit Design for Zero Energy Communication
Our ambition for 6G communication is to drastically reduce the Energy in IoT. For that purpose we aim at developing an integrated circuit enabling zero Energy communication.
The objective of this PhD is to design this circuit in FD-SOI and operating in the 2.4 GHz. In this PhD, we propose to use a new design technique which is currently revolutionizing the radio-frequency design. We expect that many innovations can be carried out during this PhD by combining those two innovations.
The candidate will integrate a large design team and he will participate in collaborative project at european level. As a first step, he will analyze the system constraints to choose the best architecture and derive the specifications. Then, he will formalize mathematically the performances of the backscattering technique in order to setup a design methodology. Then he will be working full time on circuit design, sending to fabrication two circuits in 22 um technology. He will be also involve in the test of the circuit as well as in the preparation of a demonstrator of the backscattering techniques. We expect to publish several papers in high level conferences.
Study of 3D pattern etch mechanisms into inorganic layers for optoelectronic applications
Optoelectronic devices such as CMOS Image Sensors (CIS) require the realization of 3D structures, convex microlenses, in order to focus photons towards the photodiodes defining the pixels. These optical elements are mandatory for the device efficiency. Their shape and dimension are critical for device performances. In the same way, devices based on diffractive optic and hyperspectral sensors are looking for complex multi-height structures. Finally, recent micro-display technologies for augmented reality (AR) and virtual reality (VR) require 3D structures difficult to achieve with conventional micro-fabrication technics.
Leti is at the state of the art on an alternative photolithography technics, so-called Grayscale. This process can produce a whole range of 3D structures not available with standard photolithography, such as concave, elliptic, pyramids and asymmetrical shapes. These structures could be used in a large number of application fields, like photonics and micro-displays (AR/VR). Once these structures achieved in photoresist, it is necessary to transfer them in an adapted functional layer using plasma etching. The etch mechanisms behind the transfer of micrometric 3D patterns into a polymer layer have been recently studied at Leti. To address new application needs, it is interesting to transfer these structures into silicon based inorganic layers because of their optical properties. Furthermore, the 3D pattern dimensions, currently few micrometers, need to be sub-micrometric for the most advanced technologies. In these condition, pattern transfer fidelity of 3D structures is even more challenging and it underlines why the etch mechanisms need to be well understood.
Currently the transfer into inorganic layers by plasma etching of submicronic 3D patterns obtained with Grayscale photolithography is not well studied in literature. Consequently, this thematic is innovative and has a real benefit. The goal of this PhD thesis is to study and understand the etch mechanisms in order to control the shape and dimension of the transferred structures. The work will be very experimental and will be mainly performed in Leti’s 300mm cleanroom. You will have access to a last generation plasma etch tool and numerous characterization technics. This thesis is in collaboration with the photolithography department and in interaction with different teams, such as the silicon platform and application department.