Activation‐Integrated and Memory‐Assisted Dynamic‐Latch Quantizer for Variation‐Tolerant and Low‐Energy Neuromorphic Computing

Analog‐to‐digital converters (ADCs) remain the dominant area/energy bottleneck in neuromorphic computing (NC) systems. In this work, we propose a dynamic latch–based memory‐assisted ADC that integrates charge‐trap flash (CTF) memory cells to achieve highly compact and energy‐efficient operation. Memory‐assisted dynamic‐latch ADC that embeds a CTF cell in the differential comparator, turning the threshold into a nonvolatile, electrically programable parameter and reusing it to perform in‐ADC nonlinear activation. The 4‐bit ADC tunes its switching point with 10 mV resolution via selective program/erase, exhibits mean DNL = 0.07 LSB, and operates stably up to 100°C. At 500 nm, a unit‐bit conversion requires ∼15 ns and ∼220 fJ per precharge‐and‐evaluate cycle, while scaling to 32 nm reduces the latency to ∼26 ps and the energy to ∼690 aJ. Benchmarks confirm that the proposed ADC achieves superior energy and area efficiency compared to prior designs, establishing it as a compact and viable solution for NC systems.