ML for Analog Design: Good Progress, But More to Do

Analog and mixed-signal (AMS) blocks are often critical and time-consuming part of System-on-Chip (SoC) design, due to the largely manual process of circuit design, simulation and SoC integration iterations. There have been numerous efforts to realize AMS blocks from specification by using a process analogous to digital synthesis, with automated place and route techniques [1], [2], but although very effective within their application domains, they have been limited in scope.AMS block design process, outlined in Figure 1, starts with the derivation of its target performance specifications (gain, bandwidth, phase margin, settling time, etc.) from system requirements, and establishes a simulation testbench. Then, a designer relies on their expertise to choose the topology that is most likely to achieve the desired performance with minimum power consumption. Circuit sizing is a process of determining schematic-level transistor widths and lengths to attain the specifications, with minimum power consumption. Many of the commonly used analog circuits can be sized by using well- established heuristics to achieve near-optimal performance [3]–[5]. The performance is verified by running simulations, and there has been a notable progress in enriching the commercial simulators to automate the testbench design. Machine learning (ML) based techniques have recently been deployed in circuit sizing to achieve optimality without relying on design heuristics [6]–[8]. Many of the commonly employed ML techniques require a rich training dataset; reinforcement learning (RL) sidesteps this issue by using agent that interacts with its simulation environment through a trial-and-error process that mimics learning in humans. In each step, the RL agent, which contains a neural network, observes the state of the environment and takes a sizing action. The most time-consuming step in a traditional design procedure is layout, which is typically a manual iterative process. Layout parasitics degrade the schematic-level performance, requiring circuit resizing. However, the use of circuit generators, such as the Berkeley Analog Generator (BAG) [9] automates the layout iterations. RL agents have been coupled with BAG to automate the complete design process for a fixed circuit topology [7]. Simulations with post- layout parasitics are much slower than schematic-level simulations, which calls for deployment of RL techniques that limit the sampled space. Finally, the process of integrating an AMS block into an SoC and verifying its system-level performance can be very time consuming.To achieve a wide adoption, an ML approach to AMS circuit design must: 1) return valid results for a wide range of target specifications and topologies, 2) understand where and why it is unable to meet certain specifications with a chosen topology and pick a new one, 3) produce layout and consider layout parasitics for complete end-to-end design, and 4) be automated, thus relying on the tool to carry most of the design effort.Recent progress in this area includes the use of standard RL agents for sizing AMS schematics, as well as their coupling to BAG-based generators. This talk identifies further opportunities for advancement of ML for AMS design.