MODELING OF THE SIGNAL CONVERTER FOR PHOTODIODE SENSOR DEVICES

The article explores approaches to implementing a signal converter for photodiode sensor devices within the concept of programmable systems-on-chip (PSoC), representing a promising direction in the development of modern sensor systems. Particular attention is paid to the application of SPICE modeling, which enables high-precision analysis of photodiode parameters and analog front-end components. One of the key features of the proposed models is the inclusion of parasitic parameters of photodiodes, such as parasitic capacitance and resistance, which significantly affect the accuracy and stability of signal conversion processes.The study also examines the impact of temperature instability, an essential factor limiting the efficiency of sensor devices. Methods for parametric analysis of signal converters are proposed, allowing not only the assessment of the influence of component parameters but also the identification of key patterns in the emergence of errors during signal conversion. The developed models were validated through SPICE simulations, which demonstrate the effects of parameter variations on the frequency response, time stability, and energy efficiency of signal circuits. An extended modeling analysis includes a detailed study of transient processes, particularly the behavior of photodiodes under various operating modes. The dynamics of charging and discharging parasitic capacitances and their influence on the overall performance of the circuit were investigated. To improve modeling accuracy, both the structural characteristics of photodiodes and the parameters of operational amplifiers within the signal path were taken into account. The main nodes of the signal converters were optimized through structural and parametric tuning of the SSC components. The results of the study demonstrate that the developed SPICE models minimize the influence of parasitic factors and ensure the stable operation of sensor devices, even under elevated temperatures and other challenging operating conditions. Additionally, the impact of parameter variations on signal nonlinearity was studied, and recommendations were provided for selecting optimal component values to enhance the energy efficiency of circuits.The proposed models can be integrated into the development of energy-efficient devices and components for the Internet of Things (IoT) ecosystem, increasing their competitiveness in the modern market.