The rapid growth of artificial intelligence (AI) and large language models has led to a surge in demand for high-speed optical transceivers within data centers and AI cluster computers. With transceiver speeds scaling from 100 Gbps to 400 Gbps to 800 Gbps, and future roadmaps pointing beyond 1.6 Tbps, efficient thermal management is critical to ensure performance, reliability, and energy efficiency.
November 12, 2024 - Laird Thermal Systems launches the OptoTEC™ MBX Series, a new line of micro thermoelectric coolers for high-performance space-constrained optoelectronic applications. Leveraging next-generation thermoelectric materials and advanced process automation, the MBX Series offers innovative configurations designed for integration into TO-Can, TOSA, and Butterfly packages.
At Laird Thermal Systems, we are committed to eco-friendly solutions for temperature stabilization for demanding applications across global medical, analytical, industrial, transportation, and telecommunications markets.
Older compressor-based systems often use high global-warming potential (GWP) HFC refrigerants like R134a and R404A. New industry requirements are moving away from the use of such refrigerants due to their environmental impact. New government restrictions on traditional and natural refrigerants are central to compressor-based systems.
Miniature optical packages provide a robust hermetic seal to protect small image-sensing components, including CMOS, CCD, IR, and X-ray detectors. Optical packages, like Transistor Outline (TO) Cans, are widely used for image-sensing applications. For high-end image sensing, micro thermoelectric coolers (TECs) can be integrated into optical packages called Optical Thermoelectric Assemblies (TEAs). This will provide deep cooling to temperatures well below ambient to minimize thermal noise and capture maximum light spectrum.
Fiber optic technology has essentially replaced copper wire in datacom and telecommunication industries. Emerging applications, including artificial intelligence and machine learning, require higher data transfer speeds and bandwidth across significant distances all at a lower cost.
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