In this work, a forming-free HfOx-based resistive random access memory (RRAM) with improved uniformity is successfully demonstrated without an additional doping process, ionic injection, or special layer deposition. The significantly enhanced performances can be attributed to the self-doping of Ge atoms from the Ge bottom electrode (BE) into the HfO2 layer during the postdeposition annealing (PDA) process. Besides, the RRAM provides a lower reset current and a higher ON/OFF ratio than the reported forming-free RRAM. These excellent properties are beneficial to the application of power-saving RRAM and its circuit operation.

A trade-off between the memory window and the endurance exists for transition-metal-oxide RRAM. In this work, we demonstrated that HfO_x/Ge-based metal-insulator-semiconductor RRAM devices possess both a larger memory window and longer endurance compared with metal-insulator-metal (MIM) RRAM devices. Under DC cycling, HfO_x/Ge devices exhibit a 100× larger memory window compared to HfO_x MIM devices, and a DC sweep of up to 20,000 cycles was achieved with the devices. The devices also realize low static power down to 1 nW as FPGA’s pull-up/pull-down resistors. Thus, HfO_x/Ge devices act as a promising candidates for various applications such as FPGA or compute-in-memory, in which both a high ON/OFF ratio and decent endurance are required.

In this work, we explore the material potential of yttrium-content GeO _x (YGO) in the design of a charge-trapping gate stack for embedded non-volatile memory (eNVM), which can share common gate stack materials with Ge logic CMOS. Based on a deep understanding of YGO, we propose a Ge eNVM with an oxidation-induced self-assembled YGO layer as the charge trapping layer. An eNVM fabricated on Ge offers a fast program/erase (P/E) speed (50 $\mu \text{s}$ and 2 ms for program and erase operations, respectively), low-voltage operation, a data retention lifetime of >10 years, and an endurance of > 10 ⁵ P/E cycles.

In this work, we investigate the self-rectifying behaviors of the resistive random access memory (RRAM) fabricated on n-Ge substrates, featuring an ultrahigh rectifying ratio ( $>$ 10 $^{\text{5}}$ ), a high ON/OFF ratio ( $>$ 500), and low operation voltages. The excellent performances of the Ge-based RRAM in this study are attributed to the Fermi-level pinning (FLP) near the valence band edge of n-Ge through the localized conductive filaments in HfO $_{\text{2}}$ . In addition, the underlying mechanism of the FLP behaviors in RRAM on n-Ge substrates is discussed. The electrical properties of the self-rectifying RRAM, including superior data retention, excellent uniformity, decent switching speed, endurance, and robust read/write disturbance immunity, are characterized comprehensively. The FLP-induced superior rectifying behaviors and excellent electrical properties of the RRAM enable the potential application of the high-density nonvolatile memory.

The Internet of Things (IoT) has proliferated ubiquitous information exchange between the physical and cyber worlds through consumer electronics, with a focus on moving computing power to edge terminals. Computing-in-memory (CIM) technology has emerged as a competitive candidate for edge computing because of its low power consumption and high performance. In order to achieve accurate inference for neural network models, it is crucial to comprehend the source of errors in the CIM-based analog computing paradigm. In this work, we analyzed the impact of random noises and output stabling times on the Programmable Linear Random Access Memory (PLRAM)-based CIM chip. Experimental results show that the impact of random noise is negligible. The output stabling time can be treated as RC delay, which is related to the weight distribution. We proposed a weight reordering strategy to achieve better performance without sacrificing computation accuracy. Experiments with a commercial 11-keyword speech recognition model show a 74.4% runtime reduction while maintaining a 95.6% classification accuracy.

This work presents the first demonstration of an embedded 0.5-Flash-0.5-RRAM (0.5F0.5R) hybrid memory on Ge, which can be efficiently integrated with advanced Ge logic devices. We explore yttrium (Y)-doped GeO $_{\textit{x}}$ (YGO) with a range of Y-doping concentrations in GeO $_{\textit{x}}$ and achieve stable multibit Flash and high mobility logic devices for embedded nonvolatile memories technology by exploiting the superior interface properties of low YGO and the charge-trapping behaviors of high YGO. The Flash and logic devices exhibit the peak electron mobility of 1168 cm $^{\text{2}}$ /Vs (equivalent oxide thickness (EOT) $=$ 18 nm) and 684 cm $^{\text{2}}$ /Vs (EOT $=$ 0.8 nm), respectively. By combining a Ge-based resistive memory, the 0.5F0.5R hybrid memory can provide more than 7 b/cell by distributing bits across different physical elements. Furthermore, the negligible disturbance between the two different memories in the 0.5F0.5R structure offers a promising solution for high-density nonvolatile memory applications.