Silvaco’s IPO Valuation Reaches $131m

EDA and digital twin developer Silvaco has recently announced its initial public offering (IPO) of 6 million shares. The company is offering these shares at a price range of $17 to $19 per share, which could potentially value the company at up to $131 million.

Silvaco is not a newcomer in the industry. With over 30 years of experience, the company has been at the forefront of developing TCAD (Technology Computer Aided Design) software, EDA (Electronic Design Automation) software, and design IP. In addition to its existing portfolio, Silvaco has now ventured into chip fab digital twin modeling. This expansion includes tools that simulate material behavior affecting semiconductor devices, design and analysis of transistor circuits, providing IP blocks for systems-on-chip (SoC) designs, and digital twin modeling tools.

The company has outlined its plans for the use of the net proceeds from the IPO. These funds will be allocated towards various purposes such as general corporate activities, working capital, selling and marketing efforts, research and product development, general administrative matters, repayment of outstanding debt, and capital expenditures.

Recently, Silvaco strengthened its partnership with Micron Technology by expanding its software license and support services. As part of this collaboration, Micron Technology made a $5 million investment in Silvaco. Dr. Babak Taheri, the CEO of Silvaco, highlighted the company's innovative approach, stating, “We have combined our expertise in semiconductor technologies with machine learning and data analysis to develop an artificial intelligence-based solution named fab technology co-optimization (FTCO) for wafer-level fabrication facilities.”

Dr. Gurtej Sandhu, principal fellow of Technology Pathfinding at Micron Technology, praised Silvaco's AI and digital twin solution, emphasizing its role in accelerating advancements in memory and storage technologies. The FTCO solution leverages manufacturing data to conduct statistical and physics-based machine learning simulations, creating a digital twin model of a wafer. This model enables customers to run simulation experiments to optimize wafer yield without the need for physical wafers, saving time and costs.