Lecture

Thermal Detectors: General Model and Applications

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Description

This lecture presents a comprehensive overview of thermal detectors, focusing on their general model and fundamental principles. The instructor begins by discussing the basic components of a thermal detector, including the probe that receives infrared optical power and the thermal reservoir that maintains a fixed temperature. Key concepts such as thermal conductivity, heat capacity, and power dissipation are introduced, along with mathematical equations that describe the heat variation in the probe. The relationship between sensitivity, bandwidth, and thermal properties is explored, highlighting the trade-offs between sensitivity and response time. The lecture also covers heat fluctuations, noise equivalent power (NEP), and the ultimate limits of thermal conductivity, emphasizing the impact of blackbody radiation. The instructor concludes by discussing the practical implications of these concepts in various applications, including thermography. Overall, this lecture provides a detailed understanding of the principles governing thermal detectors and their performance characteristics.

Official source

Ontological neighbourhood

Physics

Thermodynamics: Statistical mechanics

Information engineering

Signal processing: Digital image processing

Related concepts (32)

Fluctuation-dissipation theorem

The fluctuation–dissipation theorem (FDT) or fluctuation–dissipation relation (FDR) is a powerful tool in statistical physics for predicting the behavior of systems that obey detailed balance. Given that a system obeys detailed balance, the theorem is a proof that thermodynamic fluctuations in a physical variable predict the response quantified by the admittance or impedance (to be intended in their general sense, not only in electromagnetic terms) of the same physical variable (like voltage, temperature difference, etc.

Heat capacity

Heat capacity or thermal capacity is a physical property of matter, defined as the amount of heat to be supplied to an object to produce a unit change in its temperature. The SI unit of heat capacity is joule per kelvin (J/K). Heat capacity is an extensive property. The corresponding intensive property is the specific heat capacity, found by dividing the heat capacity of an object by its mass. Dividing the heat capacity by the amount of substance in moles yields its molar heat capacity.

Boltzmann distribution

In statistical mechanics and mathematics, a Boltzmann distribution (also called Gibbs distribution) is a probability distribution or probability measure that gives the probability that a system will be in a certain state as a function of that state's energy and the temperature of the system. The distribution is expressed in the form: where pi is the probability of the system being in state i, exp is the exponential function, εi is the energy of that state, and a constant kT of the distribution is the product of the Boltzmann constant k and thermodynamic temperature T.

Specific heat capacity

In thermodynamics, the specific heat capacity (symbol c) of a substance is the heat capacity of a sample of the substance divided by the mass of the sample, also sometimes referred to as massic heat capacity. Informally, it is the amount of heat that must be added to one unit of mass of the substance in order to cause an increase of one unit in temperature. The SI unit of specific heat capacity is joule per kelvin per kilogram, J⋅kg−1⋅K−1.

Maxwell–Boltzmann distribution

In physics (in particular in statistical mechanics), the Maxwell–Boltzmann distribution, or Maxwell(ian) distribution, is a particular probability distribution named after James Clerk Maxwell and Ludwig Boltzmann. It was first defined and used for describing particle speeds in idealized gases, where the particles move freely inside a stationary container without interacting with one another, except for very brief collisions in which they exchange energy and momentum with each other or with their thermal environment.

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