To deal with the growing demand for data storage and processing, phase‐change memory (PCM) provides one of the most promising candidates for next‐generation nonvolatile data storage and neuromorphic computing applications. A lot of effort has been made toward optimizing the materials and device design; thus, excellent device performances have been achieved including high density, fast switching speed, great endurance, and retention. In addition, the widely tunable optical characteristics of PCMs are irresistibly attractive for optoelectronic or all‐optical applications with unprecedented bandwidth, low energy consumption, and multilevel data storage. Herein, the materials system and switching mechanisms on experimental and modeling methods for PCM designs and applications are discussed. Electric‐domain and optical‐domain PCM‐based artificial synapses/neurons and their applications in neuromorphic computing are also reviewed. Finally, the future prospects and challenges of PCM‐based applications on materials, devices, algorithms, and system levels are highlighted.

Heterosynaptic plasticity is important for the implementation of biological functions in an excitatory synapse. Although plenty of devices have been developed to emulate the synaptic behaviors, the capability of processing information with high heterosynaptic plasticity remains challenging. Herein, it is reported that the electrical conductance of Li+ ion pre‐intercalated MoO3 nanosheet (LixMoO3: 0 < x < 1) can be efficiently modulated, relying on the local phase transition associated with electric‐field‐driven ionic migration. The LixMoO3‐based in‐plane synaptic device exhibits attractive nonvolatile memory performance including high switching ratio (≈500) and long‐term retention of states (>4000 s). By combining experimental studies with theoretical calculations, the anisotropic in‐plane ionic migration in LixMoO3 is revealed, which is attributed to the dissimilarity of adiabatic barriers along different crystallographic directions. A multiterminal LixMoO3 memristor responses anisotropically to input spikes, and the slope of conductance change along a‐axis is almost 7 times larger than that along c‐axis, which contributes to the emulation of heterosynaptic plasticity required for neurobiological architecture. Additionally, the device can also fulfill the in‐memory Boolean logic operations naturally. This work demonstrates the great potentials of ionic migration to develop artificial synapses, highlighting its promising applications for future brain‐inspired computing systems.

In this work, we demonstrated the high switching uniformity and 50 fJ/bit energy consumption in an amorphous silicon-based resistive switching (RS) device by inserting the AgInSbTe (AIST) layer between the silicon insulating layer and Ag top electrodes. The improved RS performance is attributed to the introduction of an Ag ion reservoir layer, which helps to suppress conducting filament overgrowth. After insertion of the AIST layer, the cumulative probability of low/high resistance states decreased from 176.8%/46.2% to 3.1%/11.9%, respectively. The advantages of promoting Ag dissolution enable the realization of fast switching speed (&lt;50 ns) and low set voltage (∼70 mV), which gives our device low energy consumption (∼50 fJ/bit). Moreover, the multi-step of set/reset analytical model of our dual-layer RS device was developed based on the formation and dissolution of the Ag-ion-based conductive filaments. Our work presents an effective method for obtaining high-performance Si-based memory for practical applications.

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Gestational diabetes is one of the challenges that many pregnant mothers face during this particular stage. Gestational diabetes is not only harmful to pregnant women, but also has adverse effects on offspring. Overcoming this difficulty is not so easy, expectant mothers need to control many aspects, from exercise, medication, and diet. This article analyzes the etiology and risk factors of gestational diabetes and makes recommendations based on this. The diet for gestational diabetes should be small and frequent meals, and the corresponding calorie intake should be based on one's body weight. The type of food should be less fat and easy to digest and eat more foods that have a low impact on blood sugar, such as whole grains, beans, cucumbers, and tomatoes. Patients with gestational diabetes are suitable for soothing aerobic exercise. Exercises ideal for pregnant women mainly include yoga, walking, gymnastics, etc. The time for each activity is generally 20 to 30 minutes.