High Performance Full-Inorganic Flexible Memristor with Combined Resistance-Switching

Wafer-Scale Ag2S-Based Memristive Crossbar Arrays with Ultra-Low Switching-Energies Reaching Biological Synapses

Memristive crossbar arrays (MCAs) offer parallel data storage and processing for energy-efficient neuromorphic computing. However, most wafer-scale MCAs that are compatible with complementary metal-oxide-semiconductor (CMOS) technology still suffer from substantially larger energy consumption than biological synapses, due to the slow kinetics of forming conductive paths inside the memristive units. Here we report wafer-scale Ag2S-based MCAs realized using CMOS-compatible processes at temperatures below 160 °C. Ag2S electrolytes supply highly mobile Ag+ ions, and provide the Ag/Ag2S interface with low silver nucleation barrier to form silver filaments at low energy costs. By further enhancing Ag+ migration in Ag2S electrolytes via microstructure modulation, the integrated memristors exhibit a record low threshold of approximately - 0.1 V, and demonstrate ultra-low switching-energies reaching femtojoule values as observed in biological synapses. The low-temperature process also enables MCA integration on polyimide substrates for applications in flexible electronics. Moreover, the intrinsic nonidealities of the memristive units for deep learning can be compensated by employing an advanced training algorithm. An impressive accuracy of 92.6% in image recognition simulations is demonstrated with the MCAs after the compensation. The demonstrated MCAs provide a promising device option for neuromorphic computing with ultra-high energy-efficiency.

Recent advances in flexible memristors for advanced computing and sensing

Conventional computing systems based on von Neumann architecture face challenges such as high power consumption and limited data processing capability. Improving device performance via scaling guided by Moore's Law becomes increasingly difficult. Emerging memristors can provide a promising solution for achieving high-performance computing systems with low power consumption. In particular, the development of flexible memristors is an important topic for wearable electronics, which can lead to intelligent systems in daily life with high computing capacity and efficiency. Here, recent advances in flexible memristors are reviewed, from operating mechanisms and typical materials to representative applications. Potential directions and challenges for future study in this area are also discussed.

Electrical switching properties of Ag2S/Cu3P under light and heat excitation

In this paper, we prepared and investigated the electrical switching behaviors of Cu3P/Ag2S heterojunction in the absence/presence of light/heat excitation. The structure exhibited bipolar memristor characteristics. The resistive switching mechanism is due to the formation of Ag conductive filaments and phase transition in Cu3P. We found that the resistance ratio (ROFF/RON) increased by a factor of 1.4/1.8 after light/heat excitation. The underlying mechanism was due to the photoelectric effect/Seebeck effect. Our results are helpful for the understanding of the resistive switching performance of Cu3P/Ag2S junctions, providing valuable insights into the factors influencing resistive switching performance and a clue for the enhancement of the memristor performance.

Self-Recovery of a Buckling BaTiO3 Ferroelectric Membrane

The characteristic of self-recovery holds significant implications for upholding performance stability within flexible electronic devices following the release of mechanical deformation. Herein, the dynamics of self-recovery in a buckling inorganic membrane is studied via in situ scanning probe microscopy technology. The experimental results demonstrate that the ultimate deformation ratio of the buckling BaTiO3 ferroelectric membrane is up to 88%, which is much higher than that of the buckling SrTiO3 dielectric membrane (49%). Combined with piezoresponse force microscopy and phase-field simulations, we find that ferroelectric domain transformation accompanies the whole process of buckling and self-recovery of the ferroelectric membrane, i.e., the presence of the nano-c domain not only releases part of the elastic energy of the membrane but also reduces the interface mismatch of the a/c domain, which encourages the buckling ferroelectric membrane to have excellent self-recovery properties. It is conceivable that the evolution of ferroelectric domains will play a greater role in the regulation of the mechanical properties of ferroelectric membranes and flexible devices.

Triphenylamine-Based Helical Polymer for Flexible Memristors

Flexible nonvolatile memristors have potential applications in wearable devices. In this work, a helical polymer, poly (N, N-diphenylanline isocyanide) (PPIC), was synthesized as the active layer, and flexible electronic devices with an Al/PPIC/ITO architecture were prepared on a polyethylene terephthalate (PET) substrate. The device showed typical nonvolatile rewritable memristor characteristics. The high-molecular-weight helical structure stabilized the active layer under different bending degrees, bending times, and number of bending cycles. The memristor was further employed to simulate the information transmission capability of neural fibers, providing new perspectives for the development of flexible wearable memristors and biomimetic neural synapses. This demonstration highlights the promising possibilities for the advancement of artificial intelligence skin and intelligent flexible robots in the future.

A high linearity and multilevel polymer-based conductive-bridging memristor for artificial synapses

Conductive-bridging memristors based on a metal ion redox mechanism have good application potential in future neuromorphic computing nanodevices owing to their high resistance switch ratio, fast operating speed, low power consumption and small size. Conductive-bridging memristor devices rely on the redox reaction of metal ions in the dielectric layer to form metal conductive filaments to control the conductance state. However, the migration of metal ions is uncontrollable by the applied bias, resulting in the random generation of conductive filaments, and the conductance state is difficult to accurately control. Herein, we report an organic polymer carboxylated chitosan-based memristor doped with a small amount of the conductive polymer PEDOT:PSS to improve the polymer ionic conductivity and regulate the redox of metal ions. The resulting device exhibits uniform conductive filaments during device operation, more than 100 and non-volatile conductance states with a ∼1 V range, and linear conductance regulation. Moreover, simulation using handwritten digital datasets shows that the recognition accuracy of the carboxylated chitosan-doped PEDOT:PSS memristor array can reach 93%. This work provides a path to facilitate the performance of metal ion-based memristors in artificial synapses.

Low power highly flexible BiFeO3-based resistive random access memory (RRAM) with the coexistence of negative differential resistance (NDR)

We demonstrated the resistive random access memory characteristics for Cu (top contact)/BFO/PMMA (active layer)/ITO (bottom electrode)/PET sheet as a flexible substrate device configuration. The device showed non-volatile bipolar resistive switching characteristics with good repeatability and the coexistence of NDR for 100 cycles or more with 0.28/3.43 mW power consumption for 1^st/100^th cycles. The device retains its read state for 10⁴ s or more and switches from LRS to HRS or vice versa for 10³ cycles with a pulse width of 100 ms for a write-read-erase-read pulse without affecting the memory characteristics. The Weibull distribution suggests that a set state is more stable than the reset state with shape factor β = 25.20. The device follows Ohmic behavior for the lower applied external field and Child square and Schottky emission for the higher external fields. The Joule heating, Sorets, and Fick's forces are responsible for the formation and rupturing of ionic filament. The coexistence of resistive switching and flexible strength of the device sustains the bending curvature of infinity, 0.2 cm, 1 cm, 1.7 cm, and 2.2 cm. The memory characteristics are retained under tensile conditions for 100 cycles or more. More interestingly, the power consumption for sustaining the NDR region with bending (19 μW) is much lower than without bending (0.19 mW). Thus, this study provides the possibility of integrating BFO with flexible substrates suitable for hybrid organic/inorganic memory structures.

Full-Inorganic Flexible Ag2S Memristor with Interface Resistance-Switching for Energy-Efficient Computing

Flexible memristor-based neural network hardware is capable of implementing parallel computation within the memory units, thus holding great promise for fast and energy-efficient neuromorphic computing in flexible electronics. However, the current flexible memristor (FM) is mostly operated with a filamentary mechanism, which demands large energy consumption in both setting and computing. Herein, we report an Ag₂S-based FM working with distinct interface resistance-switching (RS) mechanism. In direct contrast to conventional filamentary memristors, RS in this Ag₂S device is facilitated by the space charge-induced Schottky barrier modification at the Ag/Ag₂S interface, which can be achieved with the setting voltage below the threshold voltage required for filament formation. The memristor based on interface RS exhibits 10⁵ endurance cycles and 10⁴ s retention under bending condition, and multiple level conductive states with exceptional tunability and stability. Since interface RS does not require the formation of a continuous Ag filament via Ag⁺ ion reduction, it can achieve an ultralow switching energy of ∼0.2 fJ. Furthermore, a hardware-based image processing with a software-comparable computing accuracy is demonstrated using the flexible Ag₂S memristor array. And the image processing with interface RS indeed consumes 2 orders of magnitude lower power than that with filamentary RS on the same hardware. This study demonstrates a new resistance-switching mechanism for energy-efficient flexible neural network hardware.