- Cohu Inc.
Their Neon platform performs full 6-sided optical inspection of every HBM device using proprietary AI-trained software, defect recognition trained specifically on each customer’s device architecture.
You can’t buy a competitor’s system and retrain it in a quarter. The switching cost is measured in years.
The numbers:
→ $488M cash. No dilution risk.
→ Orders up 163% year-over-year Q1 2026
→ $750M pipeline. 5 customers in active qualification.
→ HBM revenue guidance raised from $15M to $80–100M in a single year
The market is pricing this as a test equipment cycle recovery.
The correct frame: the only qualified inspection bottleneck in the HBM supply chain.
Test equipment multiple: 3–4x EV/Revenue. AI infrastructure bottleneck multiple: 7–10x.
CONSTRAINT 2: Silicon photonics fabrication Copper wires are hitting their physical limit inside AI data centers.
Moving data between GPUs at the speeds AI training requires generates heat, signal loss, and power draw that copper interconnects can no longer handle efficiently. The industry’s answer is silicon photonics, lasers built directly onto chips, transmitting data as light instead of electrons.
Co-packaged optics (CPO) embeds those lasers directly into AI switches. Forecast penetration: from near-zero today to 35% of all optical networking by 2030.
Every one of those lasers is grown using a process called molecular beam epitaxy; MBE. A process that deposits semiconductor materials one atomic layer at a time, under ultra-high vacuum, with tolerances measured in atoms.
The problem: the entire industry’s MBE infrastructure was built for 150mm and 200mm wafers. Silicon photonics runs on 300mm production lines, the same wafer size used in leading-edge logic fabs.
There was no MBE system compatible with 300mm production lines.
Until
built one.
Meet ROSIE; Riber Oxide on Silicon Epitaxy is the first MBE platform engineered specifically for 300mm silicon photonics production lines. No other equipment company makes this.
The first two systems were ordered in 2025. ROSIE 2 the dual-chamber production version, goes into manufacturing in 2026.
This is Year 0 of the ramp. Analyst consensus price target: €6. Current price: €15+.
The gap exists because analysts are modeling Riber as a €40M scientific instruments company.
Not one single sell-side model contains ROSIE as a separate revenue line.
Silicon photonics is a $17B market by 2035. Riber’s current revenue: €40M. Market cap: €320M (~$340M USD).
If ROSIE becomes the production standard for 300mm silicon photonics the way MOCVD became the standard for LED manufacturing, the revenue trajectory and the multiple both re-rate from here.
Two constraints. Two chokepoints. One sits between every HBM die and every AI GPU that ships.
The other is the only equipment that can grow the lasers replacing copper in AI infrastructure. Both are being priced on the wrong metrics.
The market finds them eventually.
This is not financial advice. Do your own due diligence.
For full disclosure I haven’t taken a position myself, yet.
They are both on my watchlist. I'm considering adding one of them to mmy short-term portfolio.
look a like setup.
@ParadisLabs any thoughts? I can’t call out @aleabitoreddit since I’m blocked, apparently.
I'm also curious on other great investors perspective here: @moninvestor @Kaizen_Investor and @daniel_koss
-BP
$COHU Cohu: Strategic Pivot Toward AI Infrastructure, Thermal Test Solutions, and Investment Thesis.
Cohu is a semiconductor equipment manufacturer pivoting from its traditional base in automotive and industrial test systems toward AI infrastructure. The company is targeting the thermal management and inspection requirements of AI accelerators and High-Bandwidth Memory (HBM), with its Eclipse handler specifically designed for extreme power density applications.
The revenue mix has shifted meaningfully, with recurring streams now at 60% of sales, which reduces exposure to the cyclical volatility that has historically defined the back-end equipment sector. The addressable market has expanded to $3 billion as AI-driven complexity increases demand for advanced test and handling solutions.
Near-term risks center on pipeline conversion and margin pressure during the manufacturing ramp. The technology roadmap is credible, but the investment case requires Cohu to translate AI-related design wins into sustained earnings leverage by 2027. Failure to do so would leave the stock exposed as a re-rating candidate that never delivered the underlying fundamentals. Cohu is effectively a bet on back-end bottlenecks becoming a binding constraint in the global AI buildout, with execution on the ramp determining whether the thesis pays off.
$COHU (Bloomberg) -- (Adds shares move.)
Cohu, Inc. shares are down 8.7% in postmarket trading Thursday after the semiconductor manufacturing company reported adjusted earnings per share that missed the average analyst estimate.
SECOND QUARTER FORECAST
Sees sales $137 million to $151 million, estimate $127.1 million (Bloomberg Consensus)
FIRST QUARTER RESULTS
Adjusted EPS 1.0c vs. loss/shr 2.0c y/y, estimate 3.2c
Net sales $125.1 million, +29% y/y, estimate $122 million
Adjusted gross margin 46.5% vs. 44.2% y/y, estimate 45%
Adj. income from operations $3.21 million vs. loss $5.78 million y/y, estimate $4.95 million
Total assets $1.24 billion, +27% y/y, estimate $1.23 billion
COMMENTARY AND CONTEXT
Cohu expects second quarter 2026 sales to be in a range of $144 million +/- $7 million.
Raising FY26 high-performance computing revenue outlook to ~ $80-100 million
“We started fiscal 2026 with strong momentum, driven by accelerating AI and high-performance computing demand and improving market conditions driving an estimated test cell utilization of 78% at the end of March,” said Cohu President and CEO Luis Müller.
“Based on customer engagements and design win activity, we see significant growth ahead in AI-driven compute and have raised our FY26 revenue outlook.”
