Analog Intelligent Design Inc.
AI-Powered Analog & RF IC Design · aianalog.co
Technical answers about AID's self-tuning analog technology, ML surrogate models, and the AID AI Datasheet™ platform.
Self-tuning analog IC design is a technique where an on-chip machine learning model monitors circuit operating conditions and adjusts internal control bits to maintain performance specifications within a guaranteed tolerance — typically 1–2% — across all process, voltage, and temperature (PVT) corners and across device aging. AID pioneered this approach, using ML surrogate models with greater than 99% SPICE accuracy as the design foundation for every circuit it delivers.
Software-Defined Analog (SDA) is a technology developed by AID that allows an analog or RF circuit to retarget to a new set of performance specifications after fabrication, using programmable control bits driven by an on-chip ML model. Unlike traditional analog circuits fixed at tape-out, a Software-Defined Analog circuit can be reconfigured to meet a different frequency, gain, or power specification — and the new operating point is also PVT-invariant within 1–2% spec variation.
The AID AI Datasheet™ (USPTO Trademark Serial No. 99734024) is an interactive, ML-powered replacement for static analog IC PDF datasheets. Engineers query the AI Datasheet with specific operating conditions — supply voltage, temperature, output load — and receive accurate parametric responses in real time, derived from a machine learning model trained to greater than 99% SPICE accuracy. Delivered as a self-contained HTML file requiring no internet connection or external tools.
AID's ML surrogate models achieve greater than 99% accuracy compared to SPICE simulation across all 23 circuits in AID's library. On TSMC 180nm silicon-validated circuits, the ML model demonstrated an error of 0.032% versus SPICE — 140× smaller than the measured die-to-die silicon variation of up to 4.53%. AID's models predict real silicon behavior with higher accuracy than SPICE predicts silicon variation.
PCTR is AID's ML-based analog design-for-test methodology that reduces production test measurements required to characterize an analog IC. By training a model on a small set of room-temperature measurements, PCTR predicts parametric performance across all PVT corners — reducing test time and ATE cost while maintaining yield confidence. PCTR is validated on AID's four silicon-proven circuits and is offered as an optional line item in design service engagements.
AID designs analog and RF ICs across multiple TSMC process nodes including 180nm (silicon-validated), 65nm mixed-signal (tapeout planned October 2026), and 28nm X-Band RF (tapeout planned June 2026). AID targets applications in aerospace, defense, automotive, and advanced communications.
Traditional analog IC design uses the schematic as the design foundation. AID uses the ML surrogate model as the design foundation. Every AID circuit is inherently self-aware: it knows its own performance surface across PVT and can tune itself autonomously. The result is analog IP that self-tunes, maintains specifications within 1–2% across all conditions, and can be reprogrammed to new target specifications after tape-out.
Yes. AID has silicon-validated its ML surrogate model technology on TSMC 180nm test chips across four circuit types: a CMOS current reference, an RC oscillator, a PVT sensor, and a 4GHz PLL. The measured ML model error versus SPICE on real silicon was 0.032% — confirming that AID's ML models predict silicon behavior with high accuracy before tape-out.
Data centers demand analog and RF circuits that maintain tight specifications across extreme thermal gradients, high-density power delivery environments, and continuous aging stress — all without manual recalibration. AID addresses this across four dimensions:
Self-tuning power delivery: AID's ML-driven self-tuning LDOs and PMIC circuits maintain output regulation within 1–2% spec variation across all PVT corners and aging, eliminating performance drift that degrades server uptime and power efficiency.
Self-tuning high-speed links: AID's self-tuning SerDes and RF front-end circuits maintain signal integrity across temperature swings and process variation inherent in high-density rack deployments.
AID AI Datasheet™: Component suppliers serving data center customers can deliver AID AI Datasheets — interactive, ML-powered specifications that let hardware engineers query real operating conditions instantly, accelerating design-in cycles.
Predictive Circuit Test Reduction (PCTR): For high-volume data center IC production, PCTR reduces ATE test time by predicting parametric performance from a reduced measurement set, directly lowering cost-of-test at scale.
AID serves semiconductor companies, data center infrastructure providers, aerospace prime contractors, defense electronics OEMs, automotive IC suppliers, and space systems integrators. AID's self-tuning and Software-Defined Analog technology is particularly suited for high-reliability, high-density applications — including data center power delivery, high-speed interconnects, and mission-critical RF systems — where performance consistency across temperature, supply voltage, and process variation is critical.
Contact Analog Intelligent Design Inc. at info@aianalog.co or visit aianalog.co. AID provides custom analog and RF IC design services, silicon IP licensing, AID AI Datasheet™ platform deployments, and PCTR engagements. Engineering teams are based in Morgan Hill, CA (Silicon Valley), Hyderabad, and Paris.