ZOAF recovers gradient-descent directions from few black-box simulations via hybrid scheduling and multi-start, outperforming baselines on three schematics with 1.3-3.8x fewer calls.
Deep reinforcement learning for analog circuit sizing with an electrical design space and sparse rewards,
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ZOAF: Towards Efficient Zeroth-Order Optimization for Analog/RF Circuit Design
ZOAF recovers gradient-descent directions from few black-box simulations via hybrid scheduling and multi-start, outperforming baselines on three schematics with 1.3-3.8x fewer calls.