ID 原文 译文
2073 仿真结果表明高浓度的体区掺杂、较小的 STI 凹槽深度和更陡峭的 STI 侧壁将有助于改善 SOI 器件的抗总剂量效应性能。 The simulation results show that higher body doping con-centration, smaller STI divot depth and steeper STI sidewall will be helpful to improve the TID hardness performance of SOIdevices.
2074 模拟电路的设计重用是提高模拟与混合信号集成电路设计效率的重要途径。 Design reuse is an important means to increase the productivity of analog and mixed-siganl IC designs.
2075 本文提出了一种基于 gm/Id参数的不同工艺之间同一结构电路的设计移植方法。 Agm/Idbased resizing methodology for CMOS OpAmp's is proposed in this paper.
2076 方法的基本思想是保持移植前后电路中部分关键 MOS 管的gm/Id参数，从而使移植后电路的性能也基本保持不变。 The basic idea is to preserve the gm/Idpa-rameters of some crucial transistors in the circuit with the aim of the approximate performance preservation of the resized cir-cuit.
2077 介绍了基于 BSIM 等模型的 gm/Id匹配及移植电路参数确定方法。 The method to accurately match the gm/Idparameters based on the BSIM like model between the resized and the original circuit is presented.
2078 给出了一个 Miller 补偿两级运放及一个折叠共源共栅运放从 0.35μm 工艺到 0.18μm、0.13μm、90nm 工艺的移植仿真结果。 The resizing experiments of a two-stage Miller compensated OpAmp and a folded cascode OpAmp for theprocess migration from a 0. 35 μm CMOS technology to a 0. 18 μm, 0. 13 μm, 90nm one have been performed to validate the proposed method.
2079 与现有方法相比，本文方法可以更小的计算代价，得到性能基本相同、但功耗与面积缩减的电路。 The simulation results show that the method generates the resized circuits with almost the same performance but reduced power and area consumption at a lower computational cost compared with the existing approaches.
2080 为了克服原始教学优化算法在求解复杂多峰函数时全局寻优精度不高和过早收敛的缺点，提出一种矩形邻域结构和个体扰动的教学优化算法。 A teaching-learning-based optimization algorithm with rectangle neighborhood structure (RNTLBO)is proposed to overcome the shortcomings of low global search precision and premature convergence of the original teaching-learning-based optimization algorithm (TLBO)while handling complex multimodal functions.
2081 算法将种群空间设计为矩形结构，个体的矩形邻域由矩形厚度和围绕其的矩形区域个体决定， In the algorithm, the popula-tion space is designed as a rectangular structure, and the individual rectangular neighborhood is determined by the rectangle thickness and the individual rectangular region surrounding it.
2082 教和学两个阶段都使用邻域最优个体引导搜索，加强了算法勘探新解和开发局部最优解的能力; In both teaching and learning stages, the optimal individual inthe neighborhood is used to guide the search, which strengthens the ability of the algorithm to explore new solutions and ex-ploit local optimal solutions.