To enhance the modulation strength and enable more distinct current levels for logic-in-memory applications, exploring alternative device architectures and material systems can be beneficial:
Multilayered Ferroelectric Structures: Implementing multilayered ferroelectric structures with different polarization characteristics can provide enhanced control over the Schottky barriers and enable a wider range of current levels. By stacking ferroelectric layers with complementary properties, the modulation strength can be increased.
Hybrid Material Systems: Investigating hybrid material systems, such as integrating ferroelectrics with 2D materials or functional oxides, can offer unique functionalities for logic-in-memory applications. These hybrid systems can exhibit synergistic effects that enhance the modulation capabilities and enable more diverse current levels.
Nonlinear Device Architectures: Exploring nonlinear device architectures, such as memristors or synaptic transistors, can introduce additional degrees of freedom for current modulation. These devices can exhibit non-volatile behavior and complex switching characteristics, enabling a broader range of current levels for neural network computations.
Advanced Control Schemes: Implementing advanced control schemes, such as machine learning algorithms for adaptive biasing or feedback control, can dynamically adjust the device parameters to optimize the modulation strength. By leveraging intelligent control strategies, the devices can adapt to varying computational requirements and achieve precise current level tuning.
By exploring these device architectures and material systems, it is possible to enhance the modulation strength and enable more distinct current levels for logic-in-memory applications, advancing the capabilities of neuromorphic computing systems.