Publications by authors named "Haengha Seo"
Article Synopsis
- - Aluminum scandium nitride (AlScN) shows great potential for future ferroelectric memories due to its high remanent charge density, but it requires thinner films to reduce the high coercive field for lower operating voltages.
- - Thinner films encounter issues with significant leakage currents, which complicate their compatibility with existing CMOS fabrication methods.
- - This study introduces a HfN bottom electrode that minimizes lattice mismatch and reduces leakage currents, allowing for a CMOS-compatible HfN/ASN/TiN structure that showcases ferroelectric properties even at thicknesses of 3 nm and decreases the coercive voltage to 4.35 V.
Compact but precise feature-extracting ability is core to processing complex computational tasks in neuromorphic hardware. Physical reservoir computing (RC) offers a robust framework to map temporal data into a high-dimensional space using the time dynamics of a material system, such as a volatile memristor. However, conventional physical RC systems have limited dynamics for the given material properties, restricting the methods to increase their dimensionality.
View Article and Find Full Text PDFRuthenium (Ru) thin films deposited via atomic layer deposition (ALD) with a normal sequence and discrete feeding method (DFM) and their performance as a bottom electrode of dynamic random-access memory (DRAM) capacitors were compared. The DFM-ALD was performed by dividing the Ru feeding and purge steps of the conventional ALD process into four steps (shorter feeding time + purge time). The surface morphology of the Ru films was improved significantly with the DFM-ALD, and the preferred orientation of the Ru films was changed from relatively random to a <101>-oriented direction.
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