Array‐Level Integration of MoS 2– Hf 0.5 Zr 0.5 O 2 Ferroelectric Memristors for in‐Memory Image Preprocessing in Neural Networks

ABSTRACT In‐memory computing using floating‐gate memristor (FGMEM) arrays with 2D channels enables large‐scale parallelism, low energy consumption, efficient hardware‐level matrix–vector multiplications. However, tunneling‐based FGMEM faces high operating voltage, slow programming, low on/off ratio, and a small memory window. Here, we demonstrate a 16 × 16 ferroelectric memristor (FeMEM) array integrating a 2D MoS 2 channel with a hafnium–zirconium oxide (HZO) layer, enabled by metal–insulator–metal (MIM) annealing and top‐electrode etching for direct HZO–MoS 2 coupling. This enables ±1.5 V, 10 µs array‐level operation, outperforming FGMEMs (>±4 V, ∼100 ms). The array achieves 91% yield and excellent uniformity in ON–OFF current and forward/reverse threshold voltage (V th ), with Gaussian distributions within ±3σ of the mean. The FeMEM exhibits a large memory window (32.3%) and a high I on /I off ratio of 1.02 × 10 5 —about 2 and 1000 times higher than an FGMEM, respectively. It shows high linearity (β = 0.14/0.68 for long‐term potentiation/depression). Parallel‐multiply (PM) operations between input voltages (V) and conductance (G) yield an output current map (I = G × V) corresponding to a preprocessed image achieving quality gains of +13.03 dB PSNR, +0.226 SSIM, and 20 times MSE reduction after preprocessing. As an in‐memory preprocessing for a CNN, it boosts classification accuracy from 55.7% (noisy inputs) to 95.5% (excluding failed devices).