Experimental study demonstrates opto-electrically reconfigurable dual-ferroelectric stacked transistors, highlighting potential for energy-efficient multidimensional AI parallel computing.
Ferroelectric transistors have the potential to meet the computing demands of data-intensive applications such as artificial intelligence. However, the existing research lacks functional reconfigurability and multi-task parallel processing capability. Here, we report a dual-ferroelectric stacked transistor that operates through opto-electric dual-mode induced ferroelectric polarization reversal, which features configurable multi-mode functionality encompassing electronic storage, multi-level optical memory, and visual functional emulation. The dual-ferroelectric architecture significantly enhances non-volatile memory performances, demonstrating an ultra-high current on/off ratio, ultra-low dark current and energy consumption, over 8-bit conductance states, multiple analog states and large dynamic range. Furthermore, the simulated artificial visual system enables multi-task parallel processing of static and motion mixed-color information. This work demonstrates that dual-ferroelectric stacked architecture holds potential in parallel processing of multidimensional information.
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Wang et al. (2026) studied this question.
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