The terahertz (THz) range (0.1–10 THz) is increasingly exploited for imaging and spectroscopy (e.g. security scanning, medical diagnostics, non-destructive testing) because many materials are transparent to THz radiation and have unique spectral signatures. However, existing sources struggle to offer both high power and wide tunability: electronic sources (diodes, QCLs) deliver milliwatts but over narrow bands,...

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Block copolymers (BCP) are an industrial technology in full expansion, offering promising perspectives for material nanostructuring. These polymers, composed of chemically distinct block chains, self-assemble to form ordered structures at the nanometric scale. However, their current use is limited to specific nanostructuring per product (1 product = 1 nanostructuring), thus restricting their application potential. This...

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The proposed PhD work lies within the exploration of new supercapacitor and hybrid energy storage technologies, aiming to combine miniaturization, high power density, and CMOS process compatibility. The hosting laboratory (LTEI/DCOS/LCRE) has recognized expertise in thin-film integration and dielectric material engineering, offering unique opportunities to investigate ferroelectric (FE) and antiferroelectric (AFE) behaviors in doped hafnium...

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Complementary Field Effect Transistors (CFETs) represent a new generation of vertically stacked CMOS devices, offering a promising path to continue transistor miniaturization and to meet the requirements of high-performance computing. The objective of this PhD work is to study and optimize the strain engineering of the transistor channel in order to enhance carrier mobility and...

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This PhD aims to design analog liquid neural networks for generative edge intelligence. Current neuromorphic architectures, although more efficient through in-memory computing, remain limited by their extreme parameter density and interconnection complexity, making their hardware implementation costly and difficult to scale. The Liquid Neural Networks (LNN), introduced by MIT at the algorithmic level, represent a...

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Thanks to strong collaborations between teams from several research institutes and the cleanroom facilities at CEA-LETI, Grenoble has been a pioneer in the development of spin qubit devices as a platform for quantum computing. The lifetime of these spin qubits is highly sensitive to fluctuations in the qubitapos;s electrical environment, known as charge noise. Charge...

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This PhD research focuses on the integrated co-optimization of materials, fabrication processes and device architectures to enable high-performance RF transistors on advanced nanometer-scale technologies. The work aims to understand and improve key RF figures of merit—such as transit frequency, maximum oscillation frequency, noise behaviour and linearity—by establishing clear links between material choices, process innovations and...

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To increase the performance of quantum computers, three-dimensional (3D) integration is now the key! Using technologies such as flip-chip bonding, multi-layer wiring or even through-silicon vias (TSV), 3D integration offers solutions to increase the number of qubits on a processor, reduce signal loss and cross-talk and even improve thermal management. All of these aspects are...

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Recent advances in electric motors and associated power electronics have led to a significant increase in power density requirements. This increase in power density means smaller heat exchange surfaces, which amplifies the challenges associated with dissipating the heat generated by power electronics components during operation. In fact, the lack of adequate heat dissipation causes electronic...

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