Design of Nested Transconductance-Capacitance Compensation Amplifier Via Nonconvex Polynomial Optimization

We present a framework for synthesizing low-power nested transconductance (G_m)-capacitance compensation (NGCC) amplifiers through global optimization over generally nonconvex polynomials. Specifically, a nonconvex optimization problem is formulated, which is then efficiently solved through convex programming techniques based on linear matrix inequality (LMI) relaxation. Such framework allows both polynomial inequality and equality constraints, thereby facilitating more accurate transistor modelings. Compared to conventional nonlinear programming (NLP) approach such as genetic algorithm (GA), the proposed methodology exhibits superior computational efficiency, and guarantees convergence to globally optimal parameters. To demonstrate the design flow, a NGCC amplifier is designed with 0.35 mum technology and contrasted against HSPICE simulations