


default search action
"Deep Neural Network (DNN) Optimized Design of 2.45 GHz CMOS Rectifier With ..."
Wendy Wee Yee Lau, Heng Wah Ho, Liter Siek (2020)
- Wendy Wee Yee Lau
, Heng Wah Ho
, Liter Siek
:
Deep Neural Network (DNN) Optimized Design of 2.45 GHz CMOS Rectifier With 73.6% Peak Efficiency for RF Energy Harvesting. IEEE Trans. Circuits Syst. 67-I(12): 4322-4333 (2020)

manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.