The development of in‐memory computing has opened up possibilities to build next‐generation non‐von‐Neumann computing architecture. Implementation of logic functions within the memristors can ...significantly improve the energy efficiency and alleviate the bandwidth congestion issue. In this work, the demonstration of arithmetic logic unit functions is presented in a memristive crossbar with implemented non‐volatile Boolean logic and arithmetic computing. For logic implementation, a standard operating voltage mode is proposed for executing reconfigurable stateful IMP, destructive OR, NOR, and non‐destructive OR logic on both the word and bit lines. No additional voltages are needed beyond “VP” and its negative component. With these basic logic functions, other Boolean functions are constructed within five devices in at most five steps. For arithmetic computing, the fundamental functions including an n‐bit full adder with high parallelism as well as efficient increment, decrement, and shift operations are demonstrated. Other arithmetic blocks, such as subtraction, multiplication, and division are further designed. This work provides solid evidence that memristors can be used as the building block for in‐memory computing, targeting various low‐power edge computing applications.
In‐memory computation tasks of a memristive arithmetic logic unit are demonstrated based on stateful logic in a memristive crossbar. Highly reconfigurable and parallel operations are designed with simplified instructions, including Boolean logic, addition, subtraction, multiplication, division, increment, decrement, and shift operations. The energy efficiency and short latency prove its advance for future in‐memory computing applications.
Non-volatile memory (NVM) will play a decisive role in the development of the next-generation of electronic products. Therefore, the development of next-generation NVM is urgent as widely applied ...flash memory is facing its physical limit. Among various next-generation NVMs, Resistive Random Access Memory (RRAM) is a promising candidate for future memory due to its high-efficiency, high-speed and energy-saving characteristics. In recent years, continuous improvement and in-depth investigation in both materials and electrical switching mechanisms have not only lead to a breakthrough in the performance of digital NVM, but also lead to other possible memory functionality. This paper describes new findings and perspectives on various RRAM devices with different laminated structures and materials, and classifies RRAM into four categories according to different resistive switching mechanisms, from which the four elements are (1) anion-type RRAM: redox reaction and migration of oxygen ions, (2) cation-type RRAM: redox reaction and migration of cation ions, (3) carbon-based RRAM: the stretch of CC bond lengths due to oxygen and hydrogen dual ions, (4) oxide-based electrode: oxygen accumulation in oxide-based electrode.
A nitridation treatment technology with a urea/ammonia complex nitrogen source improved resistive switching property in HfO
2
-based resistive random access memory (RRAM). The nitridation treatment ...produced a high performance and reliable device which results in superior endurance (more than 10
9
cycles) and a self-compliance effect. Thus, the current conduction mechanism changed due to defect passivation by nitrogen atoms in the HfO
2
thin film. At a high resistance state (HRS), it transferred to Schottky emission from Poole-Frenkel in HfO
2
-based RRAM. At low resistance state (LRS), the current conduction mechanism was space charge limited current (SCLC) after the nitridation treatment, which suggests that the nitrogen atoms form Hf–N–Ox vacancy clusters (V
o
+
) which limit electron movement through the switching layer.
Nonvolatile stateful logic through RRAM is a promising route to build in-memory computing architecture. In this letter, a logic methodology based on 1T1R structure has been proposed to implement ...functionally complete Boolean logics. Arbitrary logic functions could be realized in two steps: initialization and writing. An additional read step is required to read out the logic result, which is in situ stored in the nonvolatile resistive state of the memory. Cascade problem in building larger logic circuits is also discussed. Our 1T1R logic device and operation method could be beneficial for massive integration and practical application of RRAM-based logic.
Biologically plausible computing systems require fine‐grain tuning of analog synaptic characteristics. In this study, lithium‐doped silicate resistive random access memory with a titanium nitride ...(TiN) electrode mimicking biological synapses is demonstrated. Biological plausibility of this RRAM device is thought to occur due to the low ionization energy of lithium ions, which enables controllable forming and filamentary retraction spontaneously or under an applied voltage. The TiN electrode can effectively store lithium ions, a principle widely adopted from battery construction, and allows state‐dependent decay to be reliably achieved. As a result, this device offers multi‐bit functionality and synaptic plasticity for simulating various strengths in neuronal connections. Both short‐term memory and long‐term memory are emulated across dynamical timescales. Spike‐timing‐dependent plasticity and paired‐pulse facilitation are also demonstrated. These mechanisms are capable of self‐pruning to generate efficient neural networks. Time‐dependent resistance decay is observed for different conductance values, which mimics both biological and artificial memory pruning and conforms to the trend of the biological brain that prunes weak synaptic connections. By faithfully emulating learning rules that exist in human's higher cortical areas from STDP to synaptic pruning, the device has the capacity to drive forward the development of highly efficient neuromorphic computing systems.
In this study, lithium‐doped silicate resistive random access memory with a titanium nitride (TiN) electrode is shown to mimic biological synapses. The TiN electrode effectively stores lithium ions, a principle widely adopted from battery construction, and enables reliable state‐dependent decay. This device offers multi‐bit functionality and synaptic plasticity, short‐term memory and long‐term memory, spike‐timing‐dependent plasticity and paired‐pulse facilitation.
More than 90% of ovarian cancer deaths are due to relapse following development of chemoresistance. Our main objective is to better understand the molecular mechanism underlying paclitaxel resistance ...(taxol resistance, Txr) in ovarian cancer. Here, we observed that the linker histone H1.0 is upregulated in paclitaxel‐resistant ovarian cancer cells. Knockdown of H1.0 significantly downregulates the androgen receptor (AR) and sensitizes paclitaxel‐resistant SKOV3/Txr and 2774/Txr cell lines to paclitaxel. Conversely, ectopic expression of H1.0 upregulates AR and increases Txr in parental SKOV3 and MDAH2774 cells. Notably, H1.0 upregulation is associated with disease recurrence and poor survival in a subset of ovarian cancer subjects. Inhibition of PI3K significantly reduces H1.0 mRNA and protein levels in paclitaxel‐resistant cells, suggesting the involvement of the PI3K/AKT signaling pathway. Knockdown of H1.0 and AR also downregulates the Txr genes ABCB1 and ABCG2 in paclitaxel‐resistant cells. Our data show that H1.0 induces GCN5 expression and histone acetylation, thereby enhancing Txr gene transactivation. These findings suggest that Txr in ovarian cancer involves the PI3K/AKT pathway and leads to upregulation of histone H1.0, recruitment of GCN5 and AR, followed by upregulation of a subgroup of Txr genes that include ABCB1 and ABCG2. This study is the first report describing the relationship between histone H1.0 and GCN5 that cooperate to induce AR‐dependent Txr in ovarian cancer cells.
To better understand the molecular mechanism underlying taxol resistance (Txr), we explored the axis PI3K/AKT/H1.0 recruiting GCN5 and AR to upregulate Txr genes, ABCB1 and ABCG2. This study is the first report describing the relationship between histone H1.0 and GCN5 which cooperate to induce AR‐dependent Txr in ovarian cancer cells.
2D materials are of particular interest in light‐to‐heat conversion, yet challenges remain in developing a facile method to suppress their light reflection. Herein, inspired by the black scales of ...Bitis rhinoceros, a generalized approach via sequential thermal actuations to construct biomimetic 2D‐material nanocoatings, including Ti3C2Tx MXene, reduced graphene oxide (rGO), and molybdenum disulfide (MoS2) is designed. The hierarchical MXene nanocoatings result in broadband light absorption (up to 93.2%), theoretically validated by optical modeling and simulations, and realize improved light‐to‐heat performance (equilibrium temperature of 65.4 °C under one‐sun illumination). With efficient light‐to‐heat conversion, the bioinspired MXene nanocoatings are next incorporated into solar steam‐generation devices and stretchable solar/electric dual‐heaters. The MXene steam‐generation devices require much lower solar‐thermal material loading (0.32 mg cm−2) and still guarantee high steam‐generation performance (1.33 kg m−2 h−1) compared with other state‐of‐the‐art devices. Additionally, the mechanically deformed MXene structures enable the fabrication of stretchable and wearable heaters dual‐powered by sunlight and electricity, which are reversibly stretched and heated above 100 °C. This simple fabrication process with effective utilization of active materials promises its practical application value for multiple solar–thermal technologies.
Inspired by the black scales of Bitis rhinoceros, a generalized approach is developed via sequential thermal actuations to construct biomimetic 2D‐material nanocoatings, including Ti3C2Tx MXene, reduced graphene oxide, and MoS2. The hierarchical MXene nanocoatings result in broadband light absorption, and realize improved light‐to‐heat performance, demonstrating extremely practical application value in solar steam generation and wearable thermal management.
Water‐based evaporative cooling is emerging as a promising technology to provide sustainable and low‐cost cold to alleviate the rising global cooling demand. Given the significant and fast progress ...made in recent years, this review aims to provide a timely overview on the state‐of‐the‐art material design and engineering in water‐based evaporative cooling. The fundamental mechanisms and major components of three water‐based evaporative cooling processes are introduced, including direct evaporative cooling, cyclic sorption‐driven liquid water evaporative cooling (CSD‐LWEC), and atmospheric water harvesting‐based evaporative cooling (AWH‐EC). The distinctive requirements on the sorbent materials in CSD‐LWEC and AWH‐EC are highlighted, which helps synthesize the literature information on the advanced material design and engineering for the purpose of improving cooling performance. The challenges and future outlooks on further improving the water‐based evaporative cooling performance are also provided.
Evaporation‐induced cooling effect is gaining significant interest due to its low carbon footprint and low or even zero electricity consumption. This review systematically introduces the fundamentals and state‐of‐the‐art material design and engineering in water‐based evaporative cooling technologies, and critically assess the challenges and offers outlooks on future development of this emerging field.
Numerous empirical studies have reported that males and females perform equally well in mathematical achievement. However, still to date, very limited is understood about the brain response profiles ...that are particularly characteristic of males and females when solving mathematical problems. The present study aimed to tackle this issue by manipulating arithmetic problem size to investigate functional significance using functional magnetic resonance imaging (fMRI) in young adults. Participants were instructed to complete two runs of simple calculation tasks with either large or small problem sizes. Behavioural results suggested that the performance did not differ between females and males. Neuroimaging data revealed that sex/gender‐related patterns of problem size effect were found in the brain regions that are conventionally associated with arithmetic, including the left middle frontal gyrus (MFG), left intraparietal sulcus (IPS) and insula. Specifically, females demonstrated substantial brain responses of problem size effect in these regions, whereas males showed marginal effects. Moreover, the machine learning method implemented over the brain signal levels within these regions demonstrated that sex/gender is discriminable. These results showed sex/gender effects in the activating patterns varying as a function of the distinct math problem size, even in a simple calculation task. Accordingly, our findings suggested that females and males use two complementary brain resources to achieve equally successful performance levels and highlight the pivotal role of neuroimaging facilities in uncovering neural mechanisms that may not be behaviourally salient.
In this fMRI study, we explore sex differences in brain activations during mathematical problem‐solving. Our findings elucidate discernible patterns of problem size effect within brain regions linked to arithmetic, with females displaying more pronounced responses than males. Machine learning analysis demonstrates the discriminability of sex based on brain signals from these regions, highlighting the complementary brain resources used by females and males to achieve comparable performance levels and emphasizing the importance of neuroimaging in uncovering underlying neural mechanisms.
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