In recent years, there has been a growing interest in developing capacitive biosensors using Complementary Metal Oxide Semiconductor (CMOS) technology for various life science applications including cellular growth monitoring, DNA hybridization detection, and drug tests. By offering the advantages of low complexity and high precision, CMOS capacitive sensor arrays can be considered as the best candidate for high throughput screening of cellular activities. Since the cultured cells are sensitive to temperature, monolithic implementation of capacitive and temperature sensors on a single chip can increase the accuracy of the system and its immunity against external noises and interferences. The aim of this thesis is to design a multi-sensor consisting of a capacitive biosensor and a temperature sensor in CMOS technology in order to conduct cellular analysis as a part of a lab-on-chip (LoC) platform. In this regard, a primary circuit and also an innovative structure are proposed for each sensor. The capacitive biosensor interfaces are designed based on charge based capacitance measurement (CBCM) method. The first circuit presented as the capacitive biosensor interface, in the first part of this thesis, is a preliminary attempt to improve the resolution and performance of fully differential core-CBCM circuits. Thereafter, a current-mode core-CBCM capacitive sensor is proposed to improve the input dynamic range (IDR) of this kind of capacitive sensors. Based on the simulation results, the proposed circuit offers an IDR ranging from 873aF to 70fF with a resolution about 10aF. The second part of this thesis investigates MOSFET-based smart temperature sensors. The primary design of the smart temperature sensor includes a temperature to current converter and a current-controlled oscillator presented in the literature along with a suitable counter. Thereafter, a novel temperature to frequency converter is proposed which is highly linear in typical conditions. The relative inaccuracy of this temperature to frequency converter (the ratio of inaccuracy to input temperature dynamic range) is about 0.71%.
