TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Uhlmann, Yannick
A1  - Essich, Michael
A1  - Schweikardt, Matthias
A1  - Scheible, Jürgen
A1  - Curio, Cristóbal
T1  - Machine learning based procedural circuit sizing and DC operating point prediction
T2  - SMACD / PRIME 2021 : International Conference on SMACD and 16th Conference on PRIME; 19-22 July 2021, online, proceedings
N2  - This paper presents a machine learning powered, procedural sizing methodology based on pre-computed look-up tables containing operating point characteristics of primitive devices. Several Neural Networks are trained for 90nm and 45nm technologies, mapping different electrical parameters to the corresponding dimensions of a primitive device. This transforms the geometric sizing problem into the domain of circuit design experts, where the desired electrical characteristics are now inputs to the model. Analog building blocks or entire circuits are expressed as a sequence of model evaluations, capturing the sizing strategy and intention of the designer in a procedure, which is reusable across different technology nodes. The methodology is employed for the sizing of two operational amplifiers, and evaluated for two technology nodes, showing the versatility and efficiency of this approach.
KW  - machine learning
KW  - neural networks
KW  - gm over id
KW  - analog ic sizing
Y1  - 2021
UR  - https://ieeexplore.ieee.org/document/9547948
SN  - 978-3-8007-5588-2
SB  - 978-3-8007-5588-2
SP  - 188
EP  - 191
S1  - 4
PB  - VDE Verlag
CY  - Berlin
ER  -