基于CMOS忆阻器仿真器的自适应尖峰模数数据转换的设计,作为自x层的最低层

IF 0.8 Q4 INSTRUMENTS & INSTRUMENTATION Journal of Sensors and Sensor Systems Pub Date : 2022-08-10 DOI:10.5194/jsss-11-233-2022
H. Abd, A. König
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引用次数: 3

摘要

摘要现代设备中使用的传感器数量正在迅速增加,与传感器的交互需要模数数据转换(ADC)。前沿技术中的传统ADC由于信号波动、制造偏差、噪声等而面临许多问题。ADC的设计者正转向时域和数字设计技术来处理这些问题。这项工作追求一种新的自适应尖峰神经ADC(SN-ADC)设计,该设计具有很好的特点,例如技术缩放问题、低电压操作、低功率和噪声鲁棒调节。SN-ADC使用尖峰时间来携带信息。因此,它可以有效地转化为积极的新技术,以实现可靠的先进传感电子系统。SN-ADC支持物联网(IoT)和工业4.0所需的self-x(自校准、自优化和自我修复)和机器学习。我们设计了SN-ADC的主要部分,它是一个自适应尖峰到数字转换器(ASDC)。ASDC基于自适应互补金属氧化物半导体(CMOS)忆阻器。它模仿生物突触的功能,长期可塑性和短期可塑性。我们设计的关键优势是完全局部无监督的自适应方案。自适应方案由两个层次结构层组成;第一层是自适应的,第二层是手动处理的。在我们之前的工作中,适应过程基于96个变量。因此,纠正突触的重量需要相当长的适应时间。本文提出了一种新的自适应方案,将变量数量减少到只有四个,并且与以前的实现相比,具有更好的自适应能力和更少的延迟时间。我们之前的工作和本次工作的最大适应时间为15 h和27 最小值与1 最小值和47.3 s.目前的赢家通吃(WTA)电路存在问题,设计成本高,并且无法识别闭合尖峰。因此,提出了一种新型的带存储器的WTA电路。它使用352个晶体管作为16个输入,可以处理最小时间差为3的尖峰 ns。ASDC已经在静态和动态变化下进行了测试。SN-ADC参数的漏码数(NOMC)、积分非线性(INL)和微分非线性(DNL)的标称值分别为0.4和0.22 LSB,其中LSB代表最低有效位。然而,这些值由于动态和静态偏差而降低,最大模拟变化等于0.88和4 分别用于DNL、INL和NOMC的LSB和6个代码。自适应将SN-ADC参数重置为标称值。所提出的ASDC是使用X-FAB 0.35设计的 µm CMOS技术和Cadence工具。
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Design of a CMOS memristor emulator-based, self-adaptive spiking analog-to-digital data conversion as the lowest level of a self-x hierarchy
Abstract. The number of sensors used in modern devices is rapidly increasing, and the interaction with sensors demands analog-to-digital data conversion (ADC). A conventional ADC in leading-edge technologies faces many issues due to signal swings, manufacturing deviations, noise, etc. Designers of ADCs are moving to the time domain and digital designs techniques to deal with these issues. This work pursues a novel self-adaptive spiking neural ADC (SN-ADC) design with promising features, e.g., technology scaling issues, low-voltage operation, low power, and noise-robust conditioning. The SN-ADC uses spike time to carry the information. Therefore, it can be effectively translated to aggressive new technologies to implement reliable advanced sensory electronic systems. The SN-ADC supports self-x (self-calibration, self-optimization, and self-healing) and machine learning required for the internet of things (IoT) and Industry 4.0. We have designed the main part of SN-ADC, which is an adaptive spike-to-digital converter (ASDC). The ASDC is based on a self-adaptive complementary metal–oxide–semiconductor (CMOS) memristor. It mimics the functionality of biological synapses, long-term plasticity, and short-term plasticity. The key advantage of our design is the entirely local unsupervised adaptation scheme. The adaptation scheme consists of two hierarchical layers; the first layer is self-adapted, and the second layer is manually treated in this work. In our previous work, the adaptation process is based on 96 variables. Therefore, it requires considerable adaptation time to correct the synapses' weight. This paper proposes a novel self-adaptive scheme to reduce the number of variables to only four and has better adaptation capability with less delay time than our previous implementation. The maximum adaptation times of our previous work and this work are 15 h and 27 min vs. 1 min and 47.3 s. The current winner-take-all (WTA) circuits have issues, a high-cost design, and no identifying the close spikes. Therefore, a novel WTA circuit with memory is proposed. It used 352 transistors for 16 inputs and can process spikes with a minimum time difference of 3 ns. The ASDC has been tested under static and dynamic variations. The nominal values of the SN-ADC parameters' number of missing codes (NOMCs), integral non-linearity (INL), and differential non-linearity (DNL) are no missing code, 0.4 and 0.22 LSB, respectively, where LSB stands for the least significant bit. However, these values are degraded due to the dynamic and static deviation with maximum simulated change equal to 0.88 and 4 LSB and 6 codes for DNL, INL, and NOMC, respectively. The adaptation resets the SN-ADC parameters to the nominal values. The proposed ASDC is designed using X-FAB 0.35 µm CMOS technology and Cadence tools.
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来源期刊
Journal of Sensors and Sensor Systems
Journal of Sensors and Sensor Systems INSTRUMENTS & INSTRUMENTATION-
CiteScore
2.30
自引率
10.00%
发文量
26
审稿时长
23 weeks
期刊介绍: Journal of Sensors and Sensor Systems (JSSS) is an international open-access journal dedicated to science, application, and advancement of sensors and sensors as part of measurement systems. The emphasis is on sensor principles and phenomena, measuring systems, sensor technologies, and applications. The goal of JSSS is to provide a platform for scientists and professionals in academia – as well as for developers, engineers, and users – to discuss new developments and advancements in sensors and sensor systems.
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