| Project Detail |
The rising demand for complex computing in artificial intelligence (AI), machine learning, and data processing, presents a major energy challenge. In this regard, traditional silicon-based computing systems, based on the Von Neumann architecture, are becoming increasingly inefficient in terms of energy consumption. This makes it inevitable to find alternative solutions for developing noble energy efficient computing devices. Neuromorphic device which emulates the brain’s neural activities, offers a path to low energy computation. However, state-of-the-art neuromorphic devices mimic only first-order synaptic behavior, such as simple time-dependent plasticity. While the advancements are impressive, but these devices lack the ability to emulate more complex higher-order synaptic functions, such as multi-synaptic plasticity or history-dependent memory. These advanced functions are crucial for handling intricate tasks for example, decision-making, pattern recognition, and reasoning in AI. SPECIFIC aims to overcome this barrier by leveraging the properties of vacancy-ordered perovskites, which will positively contribute to the development of next-generation high-order memristors. To do so, we will exploit the tunability of perovskite materials to modulate defects, transport, and interfaces, which will be tailored to achieve second-order logic in potentially scalable bio-realistic devices. SPECIFIC seeks to establish guidelines for designing second-order memristors, integrating timing- and rate-based correlations for advanced neuromorphic computing tasks. These goals align with the European Green Deal’s goal of reducing carbon emissions, as the energy-efficient devices developed will contribute to the EU’s target of net-zero emissions by 2050. Additionally, this cutting-edge project will significantly enhance the fellows expertise in materials science, device physics and neuromorphic technology, positioning her at the forefront of next-generation computing research. |
