This paper presents a SAR converter based mixed-signal multiplier for the feature extraction of neural signals using quadratic operators. After a thorough analysis of design principles and circuit-level aspects, the proposed architecture is explored for the implementation of two quadratic operators often used for the characterization of neural activity, the moving average energy operator and the nonlinear energy operator. Programmable chips for both operators have been implemented in a HV-180 nm CMOS process. Experimental results confirm their suitability for energy computation and action potential detection and the accomplished areapower performance is compared to prior art. The NEO prototype with no added delay consumes 178 nW and digitizes both the input neural signal and the operator outcome, with no need for digital multipliers.

中文翻译：

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基于电荷分配的二次算子用于神经特征提取。

本文提出了一种基于SAR转换器的混合信号乘法器，用于使用二次算子对神经信号进行特征提取。在对设计原理和电路级方面进行了全面分析之后，对提出的体系结构进行了探索，以实现两个通常用于表征神经活动的二次算子，移动平均能量算子和非线性能量算子。两家运营商的可编程芯片已在HV-180 nm CMOS工艺中实现。实验结果证实了它们适用于能量计算和动作电位检测，并将已完成的区域功率性能与现有技术进行了比较。没有增加延迟的NEO原型消耗178 nW，并且可以将输入神经信号和操作员结果数字化，而无需数字乘法器。