A Compact 0.9μ W Direct-Conversion Frequency Analyzer for Speech Recognition With Wide- Range Q-Controllable Bandpass Rectifier

Abstract

The development of ultralow-power analog front ends for edge artificial intelligence (AI) is actively pursued; however, these front ends suffer from low-frequency selection accuracy, leading to increased training loads for the AI components and higher testing costs. In this article, we propose a novel circuit that fundamentally addresses these issues through direct conversion. By re-evaluating the circuit configurations of the multiplier, harmonic removal filter, and full-wave rectifier (FWR) from scratch, we have miniaturized and integrated an ultralow-power converter that transforms frequency components into pulse sequences. The frequency to be analyzed is determined by the local frequency input to the multiplier, which can be digitally controlled with high precision. In our system, the Q value is adaptively adjusted by the local frequency of the direct conversion, allowing the same circuit configuration to be applied to all frequency nodes, eliminating the need for filter design for each node and providing a highly design-friendly and scalable frequency analysis system.The test chip was fabricated with a 0.18- $\mu $ m process, operating at a 1.2-V supply, and outputting power pulse streams corresponding to 11 different frequencies ranging from 500 to 5 kHz. The total operating power was $0.9\mu $ W, with an achieved equivalent Q factor ranging from 3.6 to 36. In a training experiment using a convolutional neural network (CNN) speech recognition model constructed with a functional model equivalent to this front end, a recognition rate exceeding 80% was achieved, demonstrating the practicality of this front end.