Microelectronics design is often a labor-intensive process involving extensive simulations, fabrication, and testing, particularly in analog design, which demands a skilled workforce with specialized knowledge. Emerging computing paradigms, such as neuromorphic and probabilistic computing, aim to harness the analog characteristics of devices for significant performance improvements over traditional methods. This presents a unique codesign challenge across the design stack, encompassing analog, mixed-signal, and beyond-CMOS devices. In this work, we introduce AI-guided codesign automation techniques, for the design of novel devices and circuits tailored for these cutting-edge computing paradigms, facilitating innovative solutions and hardware-aware algorithms for next-generation heterogeneous architectures.

We demonstrate device codesign using reinforcement learning for probabilistic computing applications. We use a spin orbit torque magnetic tunnel junction model (SOT-MTJ) as the device exemplar. We leverage reinforcement learning (RL) to vary key device and material properties of the SOT-MTJ device for stochastic operation. Our RL method generated different candidate devices capable of generating stochastic samples for a given exponential distribution.