The Role of Machine Learning in Analog Circuit Design

Key Takeaways

• The design of an analog circuit is dependent on the designer’s instincts, knowledge, and experience.

• Machine learning opens up a new perspective on using computational intelligent EDA tools for analog circuit design.

• The application of machine learning to analog circuit design builds a functional model that approximates the functional behavior of the circuit or device to be designed.

Machine learning in analog circuit design can automate the design process

To keep up with the fast-paced advancements of technology, modern electronic circuit designs are supported by electronic design automation (EDA) tools. These tools help designers design printed circuit boards and integrated circuits. In analog circuit design using EDA tools, the designer selects the required blocks from a netlist and places them on the circuit board or silicon die. The designer then translates the blocks into a circuit, and the placement of components, routing, topology selection sizing, and optimization varies with the designer’s experience and personal preference.

Applying machine learning to EDA tools is critical in analog circuit design, as it minimizes the need for humans to be a part of the design process. Machine learning offers suggestions and predictions for circuit design based on the EDA tool’s training. Machine learning in analog circuit design can automate the design process and allow the netlist to be converted to a physical circuit layout in minimum time and without human intervention. Let’s learn more about the benefits of applying machine learning to analog circuit design. 

Conventional Analog Circuit Design

Conventional analog circuit design starts with a targeted specification and analysis. The design of the analog circuit is dependent on the designer’s instincts, knowledge, and experience. Once the circuit is designed, it is simulated. If the simulation results satisfy the specification, the design is fixed. Otherwise, the design undergoes several iterations until the targeted specification is met. As a designer’s expertise and experience in circuit design increases, the number of design iterations decreases. Incorporating machine learning techniques in analog circuit design can further minimize design cycle time and reduce the time to bring a finished product to market. 

Machine Learning in Analog Circuit Design

Early computational intelligence was mainly used for speech recognition, image recognition, traffic prediction, and product recommendations. However, machine learning offers a new perspective on the application of computational intelligent EDA tools for analog circuit design.

Machine learning-based analog circuit design was developed to build a physical circuit layout from a netlist in minimum time and without human intervention. Analog design automation mimicks an experienced designer by using machine learning algorithms, previous design data, and prior knowledge to aid designers in every step of the process—from modeling to testing.

With machine learning, the designer gets hints and tips for design, synthesis, and optimization, which minimizes the time it takes to design a circuit. The application of machine learning to analog circuit design builds a functional model that approximates the functional behavior of the circuit or device to be designed. The approximation is based on the available set of data. The model is trained using this dataset. The model build can generate and predict the new outputs for new data with sufficient accuracy. Depending on the data set and how well the learning algorithms have trained the model, the accuracy of the solution varies. Machine learning in analog circuit design helps designers avoid numerous simulations. 

In the upcoming section, we will see an example of how machine learning can be applied in analog circuit design and how this application minimizes the number of trial iterations. 

An Example Application of Machine Learning

In analog circuit design, the system-level architecture and circuit specifications are determined first. The next step is to choose the topologies of blocks used in the circuit: amplifiers, comparators, or oscillators. With a human in the design loop, the complexity of the topology selection is alleviated by classifying the choices based on variations. The designer’s experience and intuition play important roles in making the topology selection.

Applying machine learning in analog circuit design provides a viable alternative for finding the solutions for topology selection. The learning algorithms and training based on previous design data and knowledge help EDA tools mimic the style of a human expert in topology selection. The machine learning-enabled EDA tools make predictions on the topology, which makes selection a much simpler task.

Cadence offers machine learning techniques that help provide better productivity, reduce time-to-market, and require less manual intervention in the design process. If you’re looking to learn more about how Cadence has the solution for you, talk to our team of experts.