基于DCResNet的SAR图像车辆目标识别

合成孔径雷达（SAR）图像自动目标识别中,特征提取和目标分类是两个重要环节。残差网络（ResNet）作为一种较新的卷积神经网络,凭借其对目标特征的自适应学习能力,在SAR图像分类领域表现突出。本文在ResNet基础上,设计出了密集连接型残差网络（DCResNet）,用于SAR图像目标识别。DCResNet在残差模块中增加了跳跃性连接的密度,不仅继承了ResNet的易学习的优点,还加强了特征的传播和利用率。除此之外,DCResNet采用平均池化的方式进行下采样,抑制了SAR图像中噪声对识别精度造成的影响。关于SAR图像目标识别的实验结果证明,本文提出的DCResNet与ResNet、AlexNet相比,不仅具有更快的收敛速度和推理速度,而且目标分类的准确率更高。     

CMOS毫米波低相噪级联双锁相环频率综合器设计北大核心

采用65 nm CMOS工艺，设计了一种低相噪级联双锁相环毫米波频率综合器。该频率综合器采用两级锁相环级联的结构，减轻了单级毫米波频率综合器带内和带外相位噪声受带宽的影响。时间数字转换器采用游标卡尺型结构，改善了PVT变化下时间数字转换器的量化线性度。数字环路滤波器采用自动环路增益控制技术来自适应调节环路带宽，以提高频率综合器的性能。振荡器采用噪声循环技术，减小了注入到谐振腔的噪声，进而改善了振荡器的相位噪声。后仿真结果表明，在1.2 V电源电压下，该频率综合器可输出的频率范围为22～26 GHz,在输出频率为24 GHz时，相位噪声为-104.8 dBc/Hz@1 MHz,功耗为46.8 mW。     

Selective Surface Engineering of Perovskite Microwire Arrays

The surface of low-dimensional perovskites play a crucial role in determining their intrinsic property. Understanding their characteristics and the influence of certain surfaces is valuable in designing functional surface-engineered structures. Meanwhile, surface passivation can also be applied to stabilize and optimize the state-of-the-art perovskite-based optoelectronics. Herein, cesium lead bromide (CsPbBr₃) microwire parallel arrays are designed and fabricated with specific (100)-terminated crystal planes, which exhibit excellent photodetection performance with long-term environment stability >3000 h. Notably, it is uncovered experimentally and theoretically that environmental oxygen can not only passivate the Br-vacancy-related trap states on the (100) surface, but also create charge carrier nanochannels to enhance the (opto)electronic properties. The coupling effects between oxygen species and the specific terminated crystal planes of perovskites highlight the importance of surface engineering for designing and optimizing perovskite-based devices.     

Environment-Adaptive Meta-Channel

The fundamental transmission channels of smart wearable devices and systems should support function reconfiguration and environment cognition or self-adaption. Here, a environment-adaptive meta-channel (EAMC) is proposed that is adaptive to the near-field environment. Benefiting from the capability to manipulate environment-wave interactions, EAMC can cognize the near-field environment impurities with subwavelength resolution, in which different impurities can be distinguished and located accurately with the aid of machine learning method. Thus, EAMC can adapt itself to varying environment so that its customized function is stabilized. For instance, EAMC is used as a communication modulator to adapt to different dielectric impurities and human body contacts dynamically. The quality of transmitted picture signals with impurities will deteriorate seriously but can be repaired efficiently as the self-adaptive process of EAMC is used. Hence, the proposed EAMC could be a candidate for fundamental transmission channels for future smart wearable devices and systems.     

402～405 MHz CMOS低功耗射频收发机北大核心

基于0.18μm CMOS工艺设计了一种用于WBAN 402～405 MHz频段具有低功耗全数字锁频和灵敏度校准功能的超再生收发机。采用具有噪声抵消技术的巴伦低噪声放大器，以减少无源匹配器件数量和适应低压工作；超再生数控振荡器采用数字控制电容阵列实现频率调谐，以消除猝灭操作期间振荡器的频率漂移；采用全数字锁频环替代频率综合器，以降低传感器节点的功耗；灵敏度校准环路与自动幅度控制环路共享组件，以减小校准误差，并能够在不中断接收状态的情况下动态校准接收机灵敏度。仿真结果表明，在1 V电源电压下，接收机灵敏度为-90 dBm,功耗为1.89 mW,其中全数字锁频环功耗为78μW;发射机功耗为1.96 mW,效率为28%。     

25～28 Gbit/s CMOS高灵敏度光接收机电路设计北大核心

基于65 nm CMOS工艺设计了一种25～28 Gbit/s具有自适应均衡和时钟数据恢复功能的光接收机电路。光接收前端采用低带宽设计，以优化接收机的灵敏度；采用判决反馈均衡器，以恢复低带宽前端引入的码间干扰。为了适应不同速率和工艺角引入的码间干扰，结合SS-LMS自适应算法，实现信号的自适应均衡。无参考时钟数据恢复电路采用鉴频环路拓宽频率捕获范围，同时将半速率鉴相器嵌入均衡器中，以降低功耗和成本。后仿真结果表明，在100 fF光电二极管的寄生电容条件下，接收前端最大增益达到66 dBΩ,25%带宽处的等效输入噪声电流为15.3 pA·Hz-1/2,光接收机灵敏度为-14.5 dBm。当电源电压为1.2 V时，光接收机的整体功耗为181.1 mW。