Cu-Al-Mn形状记忆合金（SMA）价格低廉并且形状记忆效应和力学性能优异，是一种有望实现大规模工业化应用的智能材料。笔者采用激光选区熔化（SLM）成形了在室温下具有形状记忆效应的Cu-Al-Mn合金，研究了SLM成形Cu-Al-Mn合金的显微组织、相变行为、力学性能及变形行为。结果表明：马氏体类型、有序度和可逆马氏体数量对合金的形状记忆效应具有决定性作用。随着激光功率由175 W增大到325 W，合金的有序度提高，γ1'（2H）含量逐渐增多，DSC曲线放热/吸热峰强度增大，参与马氏体相变的可逆马氏体数目增加，马氏体的择优取向增强，从而导致了合金单程形状记忆效应的改善和显微硬度的增大。同时，室温拉伸曲线上的应力诱发马氏体重取向平台的斜率增加，并且由175 W的不均匀局部变形扩展的变形方式转变为375 W的均匀变形；制备了具有近全致密（99.89%）、近100%单程形状记忆效应、约780 MPa抗拉强度的Cu-Al-Mn合金，为调控Cu-Al-Mn形状记忆合金的相变温度和力学性能提供了一种可行方法。

在X荧光光谱分析中，为了解决传统谱分析方法中存在的特征峰计数率损失以及影子峰的问题，本文拟采用一种基于深度学习的长短期记忆（Long and Short Term Memory，LSTM）神经网络模型，该模型对核脉冲幅度时间序列具有较好的适用性，通过对样本的学习能够对核脉冲信号的幅度进行准确估计。鉴于核脉冲信号样本较大，模型训练效率低，特引入卷积神经网络（Convolutional Neural Network，CNN），利用其特有的卷积核结构逐层提取样本特征，能够有效减少样本数量，降低模型训练复杂度。使用粉末铁矿样品测量得到的一系列离线核脉冲序列产生模型训练所需的数据集，该数据集的64 000个条目中，44 800个用作训练集，12 800个用作验证集，余下6 400个用作测试集。实验结果表明：训练好的CNN-LSTM模型能够极大地节省训练时间，克服传统方法局部收敛的缺陷，也能够对不同程度畸变的输入脉冲进行准确的参数估计，在训练集和验证集上得到的准确率都高于99%。进一步分析计数修复结果，得到三个影子峰校正比例的平均值为91.52%，表明训练的CNN-LSTM模型对畸变脉冲产生的计数损失的校正比例约为91.52%。该模型能够有效校正因畸变脉冲幅度损失造成的影子峰，改善X射线荧光光谱中特征峰计数率精度，在X射线荧光光谱领域具有较高的应用价值。

为了适应新一代电子技术的日益发展，开发各种新型存储器显得日益重要。与传统存储器相比，新一代的存储器不但需要更高性能的记忆特性，还需要可以满足灵活性、透明性或神经形态功能等特定应用的需求。本文以有机半导体材料C8-BTBT作为半导体层，PVP量子点共混作为浮栅提出了一种有机透明存储器（透明度≥83%）。器件具有超过40 V的存储窗口，编写/擦除电流比大于10³，在10⁴ s后仍能稳定分辨开关态。本文工作为透明柔性器件提供了一种新的解决方案，并预示了它们在下一代透明有机电子领域的潜力。

针对激光混沌保密通信系统中混沌信号接收双方硬件参数难以完全匹配的问题，用长短期记忆神经网络对发射端产生的大量混沌加密信号和部分载波信号进行充分训练，最终得到一个与发射端激光器系统高度相似的神经网络模型，并用此非线性模型代替接收端进行解密，实现了2 Gbit/s的混沌同步通信。该方法明显降低了混沌同步的复杂性。探讨了不同节点数量、耦合系数以及信噪比对于同步通信的影响，结果表明同步系数可以高达0.999 966，均方根误差达到10^-3量级，误码率低至10^-10量级。最后通过图像传输系统验证了方案的可行性。

Optical neural networks (ONNs), enabling low latency and high parallel data processing without electromagnetic interference, have become a viable player for fast and energy-efficient processing and calculation to meet the increasing demand for hash rate. Photonic memories employing nonvolatile phase-change materials could achieve zero static power consumption, low thermal cross talk, large-scale, and high-energy-efficient photonic neural networks. Nevertheless, the switching speed and dynamic energy consumption of phase-change material-based photonic memories make them inapplicable for in situ training. Here, by integrating a patch of phase change thin film with a PIN-diode-embedded microring resonator, a bifunctional photonic memory enabling both 5-bit storage and nanoseconds volatile modulation was demonstrated. For the first time, a concept is presented for electrically programmable phase-change material-driven photonic memory integrated with nanosecond modulation to allow fast in situ training and zero static power consumption data processing in ONNs. ONNs with an optical convolution kernel constructed by our photonic memory theoretically achieved an accuracy of predictions higher than 95% when tested by the MNIST handwritten digit database. This provides a feasible solution to constructing large-scale nonvolatile ONNs with high-speed in situ training capability.

卷积神经网络（CNN）在新冠肺炎X射线图像分类中取得诸多成果，但卷积结构只能学习到特征图相邻空间位置的上下文信息。为了更好地结合胸部X射线图像全局信息，除了提高CNN的深度，所提网络通过设计主干网络ConvNeXt、聚合注意力模块、长短期记忆网络，更加关注全局与局部信息的交互性。对公开访问的COVID-19 Radiography Database数据集图像分类，所提网络在三分类实验中相较于基础模型ConvNeXt在准确率、精确率、召回率上分别提高1.60个百分点、1.23个百分点、1.76个百分点，且其在多项实验指标上优于Vision Transformer、Swin-Transformer，准确率、精确率、召回率、特异度分别提高到95.6%、96.03%、95.76%、97.53%。另外选用Chest X-ray数据集进一步验证所提网络泛化能力，采用Score-CAM算法验证其有效性。实验结果表明，所提网络对新冠肺炎X射线图像具有较好的分类效果。

弹热制冷是一种新型制冷技术，具有解决目前传统制冷剂制冷和蒸汽压缩制冷技术所存在的大部分问题的巨大应用潜力，被认为是最具发展前景的制冷技术之一。它具有环保、高效、节能的优点，在成本、制冷量和效率及可行性等方面具有可观的优势。但弹热器件在形变过程中占用空间大、材料使用寿命低，在实际应用中仍然需要不断改进，因此，实现更加高效环保的固态制冷技术依旧是一个巨大的挑战。本综述归纳整理了弹热材料的原理、种类和器件设计。首先介绍弹热效应的基本原理和表征方法；其次，归纳以镍钛基、铜基、铁基、铁磁体形状记忆合金以及弹性聚合物作为弹热材料实现固态制冷的研究进展和需要解决的问题；最后，对目前研制出的弹热制冷装置进行了归纳分析和讨论。

The “memory wall” of traditional von Neumann computing systems severely restricts the efficiency of data-intensive task execution, while in-memory computing (IMC) architecture is a promising approach to breaking the bottleneck. Although variations and instability in ultra-scaled memory cells seriously degrade the calculation accuracy in IMC architectures, stochastic computing (SC) can compensate for these shortcomings due to its low sensitivity to cell disturbances. Furthermore, massive parallel computing can be processed to improve the speed and efficiency of the system. In this paper, by designing logic functions in NOR flash arrays, SC in IMC for the image edge detection is realized, demonstrating ultra-low computational complexity and power consumption (25.5 fJ/pixel at 2-bit sequence length). More impressively, the noise immunity is 6 times higher than that of the traditional binary method, showing good tolerances to cell variation and reliability degradation when implementing massive parallel computation in the array.

The temperature characteristics of the read current of the NOR embedded flash memory with a 1.5T-per-cell structure are theoretically analyzed and experimentally verified. We verify that for a cell programmed with a “10” state, the read current is either increasing, decreasing, or invariable with the temperature, essentially depending on the reading overdrive voltage of the selected bitcell, or its programming strength. By precisely controlling the programming strength and thus manipulating its temperature coefficient, we propose a new setting method for the reference cells that programs each of reference cells to a charge state with a temperature coefficient closely tracking tail data cells, thereby solving the current coefficient mismatch and improving the read window.The temperature characteristics of the read current of the NOR embedded flash memory with a 1.5T-per-cell structure are theoretically analyzed and experimentally verified. We verify that for a cell programmed with a “10” state, the read current is either increasing, decreasing, or invariable with the temperature, essentially depending on the reading overdrive voltage of the selected bitcell, or its programming strength. By precisely controlling the programming strength and thus manipulating its temperature coefficient, we propose a new setting method for the reference cells that programs each of reference cells to a charge state with a temperature coefficient closely tracking tail data cells, thereby solving the current coefficient mismatch and improving the read window.