While CMOS scaling is currently reaching its limits in power dissipation and circuit density, the analogy between biology and silicon is emerging as a solution to ultra-low-power signal processing. Urgent applications involving artificial vision and audition, including intelligent sensing, appeal original energy efficient and ultra-miniaturized silicon-based solutions. While state-of-the-art is focusing on digital-oriented solutions, this paper proposes a neuromorphic analog signal processor using Izhikevich-based artificial neurons in an analog spiking modulator. A varicap-based artificial neuron is explored reducing the silicon area to \(98.6\;\upmu{\text{m}}^{2}\) and the substrate leakage to a \(1.95 \;{\text{fJ}}/{\text{spike}}\) efficiency. Post-layout simulation results are presented to investigate the high-resolution, high-speed, and full-scale dynamic range for audio signal processing applications. The proposal demonstrates a \(9 \;{\text{bits}}\) spiking-modulator resolution, a maximum of \(8\;{\text{fJ}}/{\text{conv}}\) efficiency, and a root–mean–square error of \(0.63\;{\text{mV}}_{RMS}\) .

尽管CMOS缩放目前在功耗和电路密度方面已达到其极限，但生物学和硅之间的类比正在成为超低功耗信号处理的解决方案。涉及人工视觉和听觉的紧急应用，包括智能传感，吸引了原始的节能和超小型硅基解决方案。当最新技术专注于面向数字的解决方案时，本文提出了一种使用基于Izhikevich的人工神经元在模拟峰值调制器中的神经形态模拟信号处理器。探索了一种基于varicap的人工神经元，可将硅面积减少至\（98.6 \; \ upmu {\ text {m}} ^ {2} \）并将基底泄漏减少至\（1.95 \; {\ text {fJ} } / {\ text {spike}} \）效率。提出了布局后仿真结果，以研究音频信号处理应用的高分辨率，高速和满量程动态范围。该提案展示了\（9 \; {\ text {bits}} \）尖峰调制器分辨率，最高效率为\（8 \; {\ text {fJ}} / {\ text {conv}} \}，且均方根误差为\（0.63 \; {\ text {mV}} _ {RMS} \）。