## 1 Introduction  
The market for microsensors based on micro-electromechanical systems (MEMS) technology has grown rapidly in recent years, with many attempts using the Internet of Things (IoT) to integrate various microsensors into systems for safety, health, convenience, and industry [1].  
Microsensors provide output signals based on changes in voltage, current, resistance, or capacitance by utilizing specific physical/chemical variations. Various analog front-end architectures that can convert and amplify the input signals from microsensors to an amplified voltage or digital output have been reported, for example, pressure, acceleration, humidity sensors, and so on [2; 3; 4; 5; 6; 7]. In the recent IoT environment, which demands multi-functional sensing capability, a readout circuit for multimodal sensing is required. An example of IoT utilization with a range of microsensors is environmental monitoring [8]. As environmental pollution increases due to population growth and industrial development, monitoring technology using environmental sensors is becoming increasingly important. Microsensors, such as fine dust sensors, water quality sensors, and temperature and humidity sensors, in an environmental monitoring system must use a suitable readout circuit for each sensor type to detect and respond appropriately to adverse effects on the human body and the air/water ecosystem. The conventional approach is for each type of microsensor to have individual readout circuits. Following this approach, the cost can be drastically increased as the required modality of sensors increases. To solve this problem, several research papers have presented a multimodal sensing system that shares a single reconfigurable readout circuit [9; 10; 11; 12; 13]. A combined system that provides accurate and precise performance with reconfigurable capability can reduce the system cost, the overall power consumption, and the size. To obtain an accurate multimodal sensing capability, a low-input referred noise in the readout circuit is essential. For example, a resistance or capacitance to voltage conversion through an instrumentation amplifier (IA) can have low, long-term output fluctuations caused by the effect of low-frequency noise in an amplifier. In addition, the low-frequency noise can be amplified by the IA and can limit the output dynamic range.  
To solve this problem, dynamic offset cancellation techniques, including chopper stabilization or correlated double sampling (CDS), are widely used [14; 15; 16]. Among them, chopper stabilization is preferred because the low-frequency noise density can be lowered to near the thermal noise level by modulating the low-frequency noise components. However, the chopper stabilization technique restricts the bandwidth of the amplifier and generates a ripple caused by the up-modulation of the offset, and these disadvantages should be resolved.  
This paper presents a low-noise multimodal reconfigurable sensor readout circuit for voltage/current/resistive/capacitive microsensors. The aim of the proposed circuit was to obtain multimodal characteristics and to achieve low noise for accurate sensing capability in IoT sensors, such as environmental sensors. For multimodal characteristics, we proposed a reconfigurable structure that can accommodate voltage/current/resistance/capacitive sensors.  
Also, both low-noise and wide-bandwidth characteristics were achieved by adopting multiple dynamic offset cancellation (DOC) techniques. The amplifier was designed using a chopper-stabilized multi-path topology with an AC-coupled ripple reduction loop (RRL). Bandwidth limiting caused by the chopper stabilization technique was solved using a multi-path structure amplifier [17]. The front-end stage of this circuit could be operated in the continuous-time mode or discrete-time mode. In the discrete-time mode, to eliminate the residual offset and low-frequency noise components, the correlated double sampling technique was applied.