Imec the Netherlands

Ferroelectric-capacitor-based memory (FeRAM) is a promising emerging non-volatile memory technology that offers fast access and high endurance. Recent studies have demonstrated Ferroelectric-capacitor-based memory (FeRAM) has the potential to support Non-Destructive Readout (NDRO) as well as 3D stacking, enabling read operations that do not disturb or minor disturb the stored polarization state and higher density. However, variations in ferroelectric materials and layer structures lead to diverse hysteresis behaviors, resulting in different sensing requirements. In addition, the reduced capacitor size in deeply stacked 3D architectures further decreases the available charge, making accurate readout increasingly challenging. To better understand these issues, this work presents a highly scalable direct-capacitance-conversion characterization circuit that is capable of extracting the equivalent ferroelectric capacitance from a 3D FeRAM array. Leveraging a continuous-time delta-sigma modulator (CTDSM), the proposed platform can accurately quantize small ferroelectric equivalent capacitances ranging from 0 to 72fF with low noise and high resolution, and achieves a capacitance resolution of 0.045fF<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rms</sub> while consuming only 3μW power and occupying 0.001mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> chip area under a 22nm FDSOI technology. These results demonstrate the capability to characterize 3D FeRAM arrays with up to 64 vertically stacked ferroelectric capacitors (FeCAPs) and highlight strong scalability for large-scale FeRAM evaluation and design exploration.

In surface-based biosensors, an optimal microchannel geometry combines a large surface area in the XY plane with a reduced channel height along the Z-axis. However, fabricating microchannels with heights below 25 µm and/or incorporating micropillars remains challenging using low-cost, accessible methods. Here, we propose a simple and rapid fabrication method that exploits the intrinsic surface topography generated by fused deposition modelling (FDM) 3D printing. This layer-by-layer fabrication process inherently produces surface features that were exploited to form microchannels. The substrates were characterized to determine the dimensions and geometries using scanning electron microscopy (SEM), fluorescence microscopy, confocal microscopy, and 3D reconstructions. The results showed that surface features from the 3D-printed molds were successfully transferred into polydimethylsiloxane (PDMS). When pressed to glass, the PDMS replicas form adjacent microchannels as small as 15 µm in height that provide a shallow and extensive sensing area. Flow tests confirmed the integrity of the channels and demonstrated that they could be sealed without plasma bonding. This approach demonstrates a significant advancement in the fabrication of sub-20 µm microchannels and in the development of alternatives to traditional micropillars, with high-resolution results achieved using standard, low-cost FDM printing.

We use first-principles electronic-structure and lattice-dynamics calculations to identify dynamically stable configurations of silicon-compatible superlattices with Si/O or Si/Ge monolayers and Si/2Ge bilayers that provide a materials-level handle for engineering in-plane transport anisotropy in silicon channels. Across controlled periodicities, we quantify pronounced, tunable in-plane anisotropy in transport-relevant band curvature: in Si/O stacks, the electron effective mass reaches ≈0.26m* along [1̄10] while reaching 1.6 m* along [110], whereas Si/Ge preserves in-plane symmetry and Si/2Ge favors transport along the Y axis. Bandgaps are concurrently adjustable by interface chemistry and period, with tunability ranging from ∼50 meV (Si/Ge) to ∼430 meV (Si/2Ge), enabling threshold-voltage control alongside directional transport design. Acoustic deformation potentials increase with interface density (≈11 to 24 eV), indicating stronger electron–phonon coupling and a practical trade-off between anisotropy and mobility. Interface-specific vibrational features, including high-frequency oxygen modes (20–26 THz) and tunable phonon gaps in Ge-containing stacks, further differentiate thermal transport. Together, these results provide practical guidelines based on periodicity and interface chemistry for engineering leakage-suppressing, directionally selective silicon channel materials that are directly relevant to advanced nanosheet MOSFET technologies.

Abstract The increasing demand for three-dimensional dynamic environmental perception is driving interest in solid-state frequency-modulated continuous-wave (FMCW) LiDAR technologies. However, integrated FMCW LiDAR based on either optical phased arrays (OPAs) or focal-plane arrays (FPAs) face intrinsic trade-offs that have not yet been addressed simultaneously. Here, we present a biaxial FMCW LiDAR architecture that monolithically integrates an OPA transmitter with a coherent FPA receiver, comprising over 4500 functional components and implemented on separate optical paths in a circulator-free configuration. The architecture combines the complementary strengths of OPAs and FPAs. The OPA easily supports high optical power handling and provides agile line-by-line steering enabled by an engineered line-shaped beam. The FPA can form a large receiving aperture assisted by an imaging lens and enables parallel coherent signal readout at a single wavelength. The system achieves single-wavelength three-dimensional imaging with a ranging distance of 20 m using 3.7 mW of emitted optical power. A depth resolution of 5 cm and a velocity resolution of 1 mm/s are obtained. Parallel point-cloud at 10 frames per second (fps) is demonstrated, while acquisition rates of up to 62 fps are supported by the architecture. These results position the biaxial OPA-FPA architecture as a compact and practical system-level approach for integrated FMCW LiDAR.

ABSTRACT Precision agriculture (PA) increasingly relies on advanced sensing technologies to optimize crop yield, quality, and management practices. In this study, we present a stretchable strain sensor specifically designed for monitoring fruit growth ranging from 8 mm to 20 mm in diameter, a critical window for determining the timing for fruit thinning. The sensor employs a silicone elastomer (Ecoflex 00‐30) as both substrate and encapsulant, and a liquid metal (LM) eutectic alloy (Galinstan) as a sensing/conductive channel. Uniform spray coating was used to deposit LM as a sensing channel. The sensor was reshaped into a cylindrical structure, which enabled conformity to the fruit surfaces. The sensor demonstrates stable electromechanical performance with resistance increasing from 2.4 ± 0.5 to 12.2 ± 0.5 Ω under 150% strain (axial). Environmental testing confirmed durability under UV, moisture, and temperature fluctuations. A field validation test was carried out using a manual, self‐built readout system. The sensor exhibits a promising response during the growth period from 8 to 20 mm. Results from the Lab and field indicate the sensor's durability and reliability, making it suitable for long‐term deployment in fields/orchards. The proposed sensor offers growers actionable data to optimize thinning, irrigation, and fertilization practices by providing periodic monitoring of the fruit growth.

Recent advances in computer-generated holography (CGH) have significantly improved visual quality through high-resolution rendering; however, the accompanying increase in data size has become a critical obstacle to practical deployment. Conventional image compression techniques such as JPEG and high efficiency video coding (HEVC) do not adequately account for the unique statistical and spectral characteristics of holograms, thereby limiting both compression efficiency and reconstruction quality. This study proposes an efficient compression framework specialized for digital holograms by integrating a ringing reduction technique for diffraction calculations with CompressAI, a deep learning-based image compression framework. We demonstrate that ringing artifacts are a major factor hindering efficient hologram compression, and show that neural networks can achieve superior performance by learning the intrinsic statistical and spectral features of holograms. Our method achieves a superior balance between compression efficiency and reconstruction quality compared to conventional approaches, particularly at low bit rates. Furthermore, by introducing a multi-channel input representation, our method achieves higher compression ratios.

The invasive nature of brain implants remains a major limitation in neuromodulation strategies, often leading to chronic inflammation. To address this, soft coatings are applied on rigid probes to reduce the mechanical mismatch at the interface, or flexible probes are implemented accompanied by temporary stiffeners. This study presents a hybrid strategy integrating both approaches by applying a permanent hydrogel coating onto flexible neural probes. Moreover, we utilise the applied coatings as tool for post-operative non-invasive imaging via functionalisation of the hydrogel with 5-acrylamido-2,4,6-triiodoisophthalic acid (AATIPA), a monomer that increases radiodensity. Rheological measurements confirmed that AATIPA incorporation did not significantly alter the hydrogels' mechanical properties (storage moduli ranging from 139 ± 33.5 to 186 ± 55.5 kPa). Subsequently, we show that coated flexible probes exhibited a two-fold increase in critical buckling force compared to uncoated counterparts, indicating improved mechanical robustness evidenced through enhanced insertion performance in agarose brain phantoms. The mechanical contrast supports the dual purpose of the material in our application: the coatings provide stiffness to facilitate probe insertion in the dry state, while transitioning to a compliant, soft interface upon swelling, post-implantation. Finally, the radiodense coating enabled successful visualization of the probes in the hippocampus of a mouse model using μ-CT imaging. This approach offers a promising route for improving the mechanical and imaging performance of neural implants, potentially reducing reliance on post-mortem histology and enhancing real-time feedback in neuromodulation research.