arXiv abstract link

In a groundbreaking study, researchers have made significant advancements in memory technologies by developing a novel combination of ferroelectric capacitors (FeCAPs) and memristors, enabling an efficient dual-use memory architecture capable of handling both training and inference tasks effectively. The research focuses on the potential of integrated FeCAP and memristor technologies, paving the way for next-generation memory solutions in machine learning algorithms, particularly in neuromorphic computing systems.

Figure 1a shows five polarization–electric field (P–E) hysteresis loops measured using the positive-up–negative-down (PUND) technique at ±3 V–10 kHz on 550-nm-diameter capacitors after a wake-up phase of 104 triangular pulses at ±3 V–10 kHz. The P–E plot is obtained by...

2D (BA) 2 (MA) 3 Pb 4 Cl 13 perovskite is ferroelectric at room temperature, and its residual polarization reversal can reach 10 9 cycles. Based on 2D perovskite memristors, a 2-layer feedforward electrical neural network, and a photoelectric reserve pool system are established to recognize the image recognition. The accuracy is separately 97.15% and 90.91%, further demonstrating its potential in the field of neuromorphic visual systems. Abstract Traditional artificial vision systems built using separate sensing, computing, and storage units have problems with high power consumption and latency caused by frequent data transmission between functional units. An effective approach is to transfer some memory and computing tasks to the sensor, enabling the simultaneous perception-storage-processing of light signals. Here, an optical–electrical coordinately modulated memristor is proposed, which controls the conductivity by means of polarization of the 2D ferroelectric Ruddlesden–Popper perovskite film at room temperature. The residual polarization shows no significant decay after 10 9 -cycle polarization reversals, indicating that the device has high durability. By adjusting the pulse parameters, the device can simulate the bio-synaptic long/short-term plasticity, which enables the control of conductivity with a high linearity of ≈0.997. Based on the device, a two-layer feedforward neural network is built to recognize handwritten digits, and the recognition accuracy is as high as 97.150%. Meanwhile, building optical–electrical reserve pool system can improve 14.550% for face recognition accuracy, further demonstrating its potential for the field of neural morphological visual systems, with high density and low energy loss.

Over the past decades, electronics engineers have developed a wide range of memory devices that can safely and efficiently store increasing amounts of data.

Two-dimensional-material-based memristor arrays hold promise for data-centric applications such as artificial intelligence and big data. However, accessing individual memristor cells and effectively c...