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Quintessent bags $40M to develop lasers for AI clusters

Optical interconnect startup Quintessent Inc. today announced that it has raised $40 million in funding to ramp up production of its hardware. Cycle Capital led the Series A raise. It was joined by Goldman Sachs, Susquehanna International Group, Osage University Partners and several others. The investment follows a $11.4 million seed round in 2024. Data […] The post Quintessent bags $40M to…

Quintessent bags $40M to develop lasers for AI clusters

Startup Quintessent Inc. secured $40 million in funding to produce advanced lasers for artificial intelligence systems. The Series A funding was led by Cycle Capital, joined by other investors including Goldman Sachs, Susquehanna International Group, and Osage University Partners. This investment builds on a $11.4 million seed round in 2024.

Data centers connect AI chips via fiber-optic cables, transmitting information as light. Quintessent's comb laser, which generates light using quantum dots, is touted as more durable and efficient than existing light sources. Unlike quantum wells typically found in rival lasers, Quintessent’s quantum dots are spherical nanostructures made of a single material.

This design allows for faster electron movement, enabling quicker laser pulse generation and higher network performance. Additionally, the comb laser requires fewer supporting components and uses 40% less power, with improved reliability in high temperatures. The technology can generate light beams with eight different wavelengths, and the underlying tech can support more wavelengths and frequencies.

Quintessent's CEO, Alan Liu, stated that the company is transitioning from tech development to a product-focused business with the new funding. The investment will be used to ramp up manufacturing of the comb laser and make reliability improvements. In the future, the company plans to develop other optical products, such as signal amplifiers to enhance connection reliability.

Written by urgent.news from SiliconANGLE's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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