SHMD News & Events

SCHMID Group files to sell 5M ordinary shares for holders

Sees FY26 adjusted EBITDA margin exceeding 12% and order intake of approximately EUR114M

SCHMID Group provided an update on its operational development following the completion of Q1. In Q1, the company recorded order intake of EUR13.6M and generated revenues of EUR18.2M. Q1 reflects generally the softest period in terms of order intake and revenue contribution. The order book stood at EUR49M at the end of the quarter. SCHMID reaffirms its FY26 revenue guidance exceeding EUR100M, an Adjusted EBITDA margin exceeding 12% and order intake of approximately EUR114M. Following the issuance of the second tranche of the $30M convertible notes financing on March 5, the purchasers of the convertible notes have issued six separate conversion notices converting a total of $12M in principal amount for an aggregate of 2,197,898 new ordinary shares of the company. As a result, the outstanding number of shares has increased to 57,800,909. On April 24, SCHMID Group entered into separate subscription agreements and separate set-off agreements with Anette Schmid, Christian Schmid, Christine Schmid and Schmid Grundstucke GmbH to off-set financial liabilities in an aggregate amount of EUR30.75M. In connection with these agreements, the company entered into debt assumption agreements with the its fully-owned subsidiary, Gebr. Schmid GmbH.

SCHMID Group secured a lower two-digit million-USD purchase order from a major Asian high-end PCB manufacturer for a new HDI multilayer production line. The systems will support the manufacturing of next-generation AI server boards and high-performance computing platforms, one of the fastest-growing segments of the electronics industry. The customer is a leading high-technology PCB manufacturer in China with strong exposure to global hyperscale and data center infrastructure markets. The order covers multiple wet process systems designed for high-layer-count HDI multilayer boards, addressing the increasing complexity and reliability requirements of AI and HPC hardware.