忆阻类脑计算

随着深度学习的高速发展,目前智能算法的飞速更新迭代对硬件算力提出了很高的要求.受限于摩尔定律的告竭以及冯·诺伊曼瓶颈,传统CMOS集成无法满足硬件算力提升的迫切需求.利用新型器件忆阻器构建神经形态计算系统可以实现存算一体,拥有极高的并行度和超低功耗的特点,被认为是解决传统计算机架构瓶颈的有效途径,受到了全世界的广泛关注.本文按照自下而上的顺序,首先综述了主流忆阻器的器件结构、物理机理,并比较分析了它们的性能特性.然后,介绍了近年来忆阻器实现人工神经元和人工突触的进展,包括具体的电路形式和神经形态功能的模拟.接着,综述了无源和有源忆阻阵列的结构形式以及它们在神经形态计算中的应用,具体包括基于神经网络的手写数字和人脸识别等.最后总结了目前忆阻类脑计算从底层到顶层所遇到的挑战,并对该领域后续的发展进行了展望.     

废旧动力锂电池回收利用技术的进展

简要介绍了动力锂电池的分类和回收利用流程,综述了国内外动力锂电池的化学、物理和联合回收工艺发展的现状,通过对比了各种回收工艺的特点,指出联合回收工艺是将来发展方向。     

Valence band variation in Si (110) nanowire induced by a covered insulator

In this work, we investigate strain effects induced by the deposition of gate dielectrics on the valence band structures in Si (110) nanowire via the simulation of strain distribution and the calculation of a generalized 6 × 6k$\cdot$p strained valence band. The nanowire is surrounded by the gate dielectric. Our simulation indicates that the strain of the amorphous SiO₂ insulator is negligible without considering temperature factors. On the other hand, the thermal residual strain in a nanowire with amorphous SiO₂ insulator which has negligible lattice misfit strain pushes the valence subbands upwards by chemical vapour deposition and downwards by thermal oxidation treatment. In contrast with the strain of the amorphous SiO₂ insulator, the strain of the HfO₂ gate insulator in Si (110) nanowire pushes the valence subbands upwards remarkably. The thermal residual strain by HfO₂ insulator contributes to the up-shifting tendency. Our simulation results for valence band shifting and warping in Si nanowires can provide useful guidance for further nanowire device design.