Products
14 dBm pico ITLA
Technical spec sheet
17 dBm nano ITLA
Technical spec sheet
NEW
14 dBm pico ITLA for 100G coherent transceivers
Continuous-wave tunable laser source for digital coherent edge and access applications. Fully integrated 14 dBm sub-assembly in ultra-small pico form factor.
Request brochure
Solutions
Telecom
Enterprise
Metro
Access
DCI & Scale-across
DCI
Scale-across
NEW
14 dBm pico ITLA for 100G coherent transceivers
Continuous-wave tunable laser source for digital coherent edge and access applications. Fully integrated 14 dBm sub-assembly in ultra-small pico form factor.
Request brochure
Company
Newsroom
Get in touch

Products
- 14 dBm pico ITLA
  - Technical spec sheet
- 17 dBm nano ITLA
  - Technical spec sheet
- NEW
  14 dBm pico ITLA for 100G coherent transceivers
  Continuous-wave tunable laser source for digital coherent edge and access applications. Fully integrated 14 dBm sub-assembly in ultra-small pico form factor.
  Request brochure
Solutions
- Telecom
- DCI & Scale-across
  - DCI
  - Scale-across
- NEW
  14 dBm pico ITLA for 100G coherent transceivers
  Continuous-wave tunable laser source for digital coherent edge and access applications. Fully integrated 14 dBm sub-assembly in ultra-small pico form factor.
  Request brochure
Company
Newsroom
Get in touch

Tag: photonic processors

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The Lasers Powering AI

August 21, 2024 by admin

Artificial Intelligence (AI) networks rely on vast amounts of data processed and transferred at incredible speeds to function effectively. This data-intensive nature requires robust infrastructure, with lasers playing a pivotal role. AI networks depend on two primary processes: AI training and AI inference. Training involves feeding large datasets into models to learn and make predictions,

optical-long-multi-color

How Photonics Enables AI Networks

August 7, 2024 by admin

Artificial Intelligence (AI) networks have revolutionized various industries by enabling tasks such as image recognition, natural language processing, and autonomous driving. Central to the functioning of AI networks are two processes: AI training and AI inference. AI training involves feeding large datasets into algorithms to learn patterns and make predictions, typically requiring significant computational resources.