arXiv subjects

Supriyo Maji

Publications and source records attributed to Supriyo Maji.

3 recordsLinked to original sources

arXiv2025

Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning

Layout-dependent effects (LDEs) significantly impact analog circuit performance. Traditionally, designers have relied on symmetric placement of circuit components to mitigate variations caused by LDEs. However, due to non-linear nature of these effects, conventional methods often fall short. We propose an objective-driven, multi-level, multi-agent Q-learning framework to explore unconventional design space of analog layout, opening new avenues for optimizing analog circuit performance. Our approach achieves better variation performance than the state-of-the-art layout techniques. Notably, this is the first application of multi-agent RL in analog layout automation. The proposed approach is compared with non-ML approach based on simulated annealing.

cs.AR↗

arXiv2024

Multi-Objective Optimization for Common-Centroid Placement of Analog Transistors

In analog circuits, process variation can cause unpredictability in circuit performance. Common-centroid (CC) type layouts have been shown to mitigate process-induced variations and are widely used to match circuit elements. Nevertheless, selecting the most suitable CC topology necessitates careful consideration of important layout constraints. Manual handling of these constraints becomes challenging, especially with large size problems. State-of-the-art CC placement methods lack an optimization framework to handle important layout constraints collectively. They also require manual efforts and consequently, the solutions can be suboptimal. To address this, we propose a unified framework based on multi-objective optimization for CC placement of analog transistors. Our method handles various constraints, including degree of dispersion, routing complexity, diffusion sharing, and layout dependent effects. The multi-objective optimization provides better handling of the objectives when compared to single-objective optimization. Moreover, compared to existing methods, our method explores more CC topologies. Post-layout simulation results show better performance compared to state-of-the-art techniques in generating CC layouts.

cs.AR↗

arXiv2017

Spin Transfer Torque Driven Coupled Oscillators for Self-Oscillating RF Mixers

Spin transfer torque oscillators (STOs) based on magnetic tunnel junction (MTJ) devices are emerging as a possible replacement for complementary metal-oxide semiconductors for radio-frequency (RF) signal generation. Advantages include low power consumption, small device area, and large frequency tunability. But such a single device cannot achieve the necessary noise performance for RF applications. It has been reported lately that a network of globally coupled STOs achieves significant improvement in phase noise. The study here is to propose use of such coupled STOs as self-oscillating RF mixers. Critical mixer performance parameters, including conversion gain, output power, and linearity, are discussed.

cs.ET↗