Abstract
This article introduces an adhesive interposer-based reconfigurable multi-sensor patch interface with on-chip quan- tized time-domain feature extraction, tailored for heterogeneous physiological and environmental monitoring. A proposed patch concept includes integrated micro-scale structures for comfort- able pressure-based reconfiguration, allowing easy attachment and detachment of various sensor elements. For embedding edge-computing capability into a miniaturized patch device with a conventional legacy microcontroller, its multi-sensor inter- face integrated circuit (IC) is proposed to include on-chip analog feature extraction and classification engines of quan- tized time-domain convolutional neural network (QTD-CNN) and one-shot computing binary neural network (BNN). The design employs 1-bit past-data quantization, analog normaliza- tion, and flexible time window schemes to minimize leakage problems in conventional analog engines and supports reconfig- urable operations for healthcare and environmental applications. This interface IC includes five types of readout frontends for chemo-resistive sensors, electrochemical sensors, biopoten- tials, bioimpedance, and photoplethysmogram, where every path is designed to provide both wide dynamic range (DR) and Received 18 October 2024; revised 7 February 2025; accepted 25 March 2025. Date of publication 10 April 2025; date of current version 29 October 2025. This work was supported in part by the Technology Innovation Program th