Quantum geometry, describing the inherent geometric structure of electron wavefunctions in momentum space, transcends the traditional charge degree of freedom and provides a novel physical basis for information encoding and processing. The key to such new computing paradigms is the nonvolatile electrical programming of quantum geometric states at room temperature, which, however, has not been established. Here, we demonstrate ferroelectrically switchable quantum geometry in few-layer WTe2, which uniquely enables complementary convolutional processing. By employing the intrinsic coupling between ferroelectric polarization and quantum geometry in few-layer WTe2, we show that the second- and third-order nonlinear anomalous Hall effects (NLAHE) can be deterministically and electrically switched in a nonvolatile and correlated manner. The switching is robust at room temperature for ~104 cycles and retention of ~105 s. Furthermore, leveraging the opposite switching behaviors of second- and third-order NLAHE at room temperature, we demonstrate complementary in-memory computing and implement a hardware-level complementary convolution kernel. This kernel overcomes the inherent directional specificity of conventional convolutional networks and achieves a texture recognition accuracy of 98%, thereby illustrating a viable pathway towards physics-native computing through exploiting exotic physics in quantum materials. Quantum geometry (an intrinsic property of electron wavefunctions in a material) can be used as a physical basis for computing applications, but this has been experimentally challenging so far. Here, the authors demonstrate that ferroelectrically-switchable second- and third-order nonlinear anomalous Hall effects in few-layer WTe2 can be applied for in-memory computing tasks.

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Nature

Researchers demonstrate ferroelectrically switchable quantum geometry in few-layer WTe2 at room temperature, enabling complementary in-memory computing. By exploiting intrinsic coupling between ferroelectric polarization and quantum geometry, second- and third-order nonlinear anomalous Hall effects (NLAHE) can be deterministically and nonvolatilely switched electrically. The switching is robust over ~10,000 cycles with ~100,000 seconds retention at room temperature. Leveraging opposite switching behaviors of these two NLAHE orders, a hardware-level complementary convolution kernel is implemented, achieving 98% texture recognition accuracy and overcoming directional specificity limitations of conventional convolutional networks.

Room-temperature ferroelectrically switchable quantum geometry in few-layer WTe2 for complementary in-memory computing