Synopsys Reports Q2 Fiscal 2026 Revenue of $2.276 Billion, Up 41.9% Year-over-Year

en.Wedoany.com Reported - Synopsys announced revenue of $2.276 billion for the second quarter of fiscal 2026, a 41.9% increase from $1.604 billion in the same period last year, primarily driven by the continued expansion of AI infrastructure demand, which has increased semiconductor design complexity, verification requirements, and system-level engineering workloads. The company stated that revenue exceeded previous expectations, with non-GAAP earnings per share reaching $3.35 for the quarter.

Revenue for the second quarter of fiscal 2026 was $2.276 billion, a year-over-year increase of 41.9%. GAAP earnings per share were $0.09, compared to $2.24 in the same period last year; non-GAAP earnings per share were $3.35, compared to $3.67 in the same period last year. Design Automation revenue was $1.822 billion, accounting for 80% of total revenue; Design IP revenue was $454.2 million, accounting for 20% of total revenue. The fiscal 2026 revenue guidance midpoint is $9.665 billion, with a non-GAAP earnings per share guidance midpoint of $14.76, and expected free cash flow of approximately $2 billion.

Synopsys President and CEO Sassine Ghazi stated that AI is expanding semiconductor demand, the architectural diversity and complexity of chips and the systems they drive, fueling demand growth across the company's entire product portfolio. The company's momentum, leadership roadmap, and deep customer collaborations lay the foundation for solving customers' most critical engineering challenges, achieving sustained growth and margin expansion.

These results reflect that, under the AI infrastructure trend, value is shifting towards the design stack that supports larger chips, chiplets, advanced packaging, high-speed interconnects, and system-level optimization. Furthermore, the integration of Ansys enables Synopsys to address workloads increasingly spanning silicon, power, thermal management, signal integrity, and full-system simulation, as AI platforms move from individual accelerators to rack-scale infrastructure.

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