Ferroelectric Hf₁₋ₓZrₓO₂ Materials for Energy-Efficient Memory and Edge AI Applications

Project Description The rapid growth of Edge Artificial Intelligence (Edge AI), Internet-of-Things (IoT) devices, and data-centric computing is driving an urgent need for memory technologies that combine high performance with extremely low energy consumption. Future computing systems require non-volatile memories capable of reducing power delivery requirements while providing excellent scalability, endurance, and compatibility with advanced semiconductor manufacturing. Ferroelectric materials based on Hf ₁₋ₓ Zr ₓ O ₂ (HZO) have emerged as leading candidates for next-generation memory technologies thanks to their CMOS compatibility, scalability to advanced technology nodes, and potential for low-voltage, energy-efficient operation. Continued advances in material engineering, electrode integration, and interface optimization are critical for unlocking their full potential in future memory and computing architectures. During this internship, the student will contribute to the development of advanced HZO-based ferroelectric thin films and engineered interfaces for emerging memory applications targeting energy-efficient computing and Edge AI hardware. The work will combine thin-film deposition, semiconductor processing, advanced material characterization, and electrical evaluation of memory devices. This project offers a unique opportunity to perform cutting-edge research in a world-leading semiconductor R&D environment while gaining hands-on experience with state-of-the-art fabrication and characterization techniques. Research Activities The student will: Fabricate and optimize Hf ₁₋ₓ Zr ₓ O ₂ (HZO)-based ferroelectric thin films for memory applications. Investigate the influence of electrodes and interfacial layers on ferroelectric properties and device behavior. Study material and interface engineering approaches enabling low-voltage and energy-efficient memory operation. Perform structural, chemical, microstructural, and electrical characterization of thin films and memory devices. Analyze correlations between material properties, processing conditions, and memory performance. Contribute to the development of next-generation memory technologies for Edge AI and low-power computing systems. Type of internship : PhD internship, Master internship Duration : 12 months Required educational background : Nanoscience & Nanotechnology, Energy, Electrotechnics/Electrical Engineering, Chemistry/Chemical Engineering University promotor : Jan Van Houdt (KU Leuven) Supervising scientist(s) : For further information or for application, please contact Mihaela Ioana Popovici ( Mihaela.Ioana.Popovici@imec.be ) The reference code for this position is 2026-INT-285 . Mention this reference code in your application. Only for self-supporting students. Applications should include the following information: resume motivation current study Incomplete applications will not be considered