Automated performance optimization for RF/analog IP development.

Analog IP optimization is not limited by the optimizer. It is usually limited by how repeatable the measurement loop is.

For many analog and RF blocks, the real engineering effort sits in the testbench: biasing the DUT correctly, applying realistic stimuli, defining corners, extracting meaningful metrics, and making sure every candidate is evaluated in the same way. Without reusable testbench infrastructure, every optimization run becomes a fragile one-off experiment.

A good reusable testbench standardizes the interface between design variables, simulation setup, and measured results. The optimizer should not need to understand every schematic detail. It should see a clean parameter set and a consistent metrics file.

It also captures engineering judgment. The right load transient for an LDO, the right two-tone setup for an LNA, the right jitter and channel condition for a SerDes receiver, or the right FFT window for an ADC are all part of the design knowledge. Reusable testbenches preserve that knowledge.

Most importantly, reusable testbenches make results comparable. Architecture A, architecture B, different PDK corners, and later design revisions can all be evaluated through the same measurement contract. That is what turns optimization from trying many simulations into a disciplined design flow.

An eye diagram is useful, but for 112G SerDes it is not enough.

At 112G PAM4, the link is a chain of tightly coupled impairments: TX FFE tap settings, package and channel loss, CTLE peaking, ADC resolution, clock jitter, CDR behavior, FFE/DFE adaptation, and sometimes MLSE or other sequence detection. A clean-looking eye at one point in the chain may not tell you why the link works, where the margin comes from, or what will fail across corners.

A stronger optimization flow needs stage-aware metrics. Before the receiver, we need channel insertion loss, return loss, crosstalk, and TX pre-emphasis. Inside the receiver, we need CTLE response, ADC input range, quantization noise, timing margin, and equalizer convergence. After detection, we need SER or BER estimates, error distribution, jitter tolerance, and adaptation stability.

It also needs realistic stimuli. PAM4 symbols, PRBS patterns, jitter injection, bandwidth-limited channels, and stressed channel conditions reveal problems that a simple waveform snapshot can hide.

A single eye diagram answers, does this case look open? A serious 112G SerDes optimization flow answers why it is open, how much margin exists, and which knobs actually created that margin.

RF and analog teams can benefit from AI without exposing schematics, PDK data, or proprietary design details.

The key is to keep AI close to the workflow, but far from confidential implementation data. Instead of sending full schematics or layouts to an external model, teams can expose structured, limited information: block type, target specs, allowed variables, simulation status, metric names, and anonymized failure modes.

AI is especially useful around the design loop. It can help generate test plans, suggest likely measurements, organize optimization variables, explain failed simulations, compare candidate results, and draft reports. These tasks need engineering context, but not the full circuit.

A safe architecture separates three layers: a private layer for schematics, PDK files, simulation waveforms, extracted views, and customer-specific IP; a structured interface layer for sanitized specs, corners, variable ranges, metric outputs, logs, and workflow states; and an automation layer that applies approved actions locally.

For RF/analog design, the most practical use of AI may not be generating a complete circuit. It may be helping engineers run better experiments, reuse knowledge, diagnose issues faster, and turn simulation data into decisions while keeping the real IP safely inside the design environment.