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Process mismatch analysis based on reduced-order models

机译:基于阶数模型的过程不匹配分析

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This paper describes a methodology based on reduced-order models to investigate the effects of process mismatch in analog circuits in the presence of reliability degradation. Neural network-based reduced-order models for the DC drain current, Ids, of 65 nm n- and p-channel transistors have been generated in terms of six process parameters, temperature, and device age. The models identify the contribution of process parameters to the mismatch of n- and p-channel transistors as they age. Hot carrier injection (HCI) is considered as the main reliability degradation for n-channel devices and negative bias temperature instability (NBTI) is considered for p-channel devices. It is demonstrated that the variations of the effective channel length and intrinsic threshold voltage are major contributors to device mismatch in the absence of aging. Finally, a beta multiplier current reference is analyzed using the developed models for the impact of process mismatch with and without the aging effects. It is shown that in a cascode current mirror the variability of the reference current can be reduced by ensuring that the same rail transistors experience similar variations.
机译:本文介绍了一种基于阶数模型的方法,以研究可靠性降解存在于模拟电路中的过程失配的影响。对于DC漏极电流的基于神经网络的减小型号,I DS 的65nm N-和P沟道晶体管已经在六个工艺参数,温度和设备年龄方面产生。该模型根据年龄的增长,识别过程参数对N和P沟道晶体管不匹配的贡献。热载体注射(HCI)被认为是N沟道装置的主要可靠性降解,并且对P沟道装置考虑负偏置温度不稳定性(NBTI)。证明有效通道长度和固有阈值电压的变化是在没有老化的情况下为器件不匹配的主要贡献者。最后,使用开发的模型来分析测试倍增倍增器电流参考,用于对过程不匹配的影响,没有老化效果。结果表明,通过确保相同的轨道晶体管经历类似的变化,可以减少参考电流的可变性。

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