DeepSim, Inc. GTM brain

DeepSim develops AI-driven multiscale physics simulation for engineering teams, initially focused on thermal analysis for AI chips.

category

AI semiconductor physics simulation

market profile

  • text: Thermal simulation is a bottleneck in semiconductor design, limiting how many variants engineers can evaluate.
  • text: Teams must model physical effects across chip and system scales while managing simulation setup and runtime.

personas

  • title: Semiconductor thermal simulation engineer
    pains:
    • Slow thermal models limit design iterations
    • Complex simulation setup consumes engineering time
  • title: AI chip design engineering director
    pains:
    • Need to identify thermal risks early across chip and package scales
    • Need defensible accuracy and compatibility with established design workflows
  • title: CAE or EDA infrastructure lead
    pains:
    • Large models strain existing compute and simulation capacity
    • Tool adoption depends on measured speed, accuracy and integration

icp

  • text: Semiconductor design teams developing high-power AI chips and packages with challenging thermal behavior.
  • text: Engineering organizations running large multiscale physics simulations and seeking more design iterations.

message pillars

  • text: Accelerate simulation while spanning physical scales; speedup is a company claim to validate.
  • text: Reduce tedious simulation preparation.
  • text: Increase model scale using GPU acceleration.
  • text: Return engineering time to higher-value design decisions.

objections

  • objection: Does the faster model match measured silicon and trusted solvers?
    response: Benchmark representative geometries against physical measurements and existing signoff tools before relying on results.
  • objection: Can it fit our established EDA flow?
    response: Test import, output and integration requirements on an existing chip design.
  • objection: Is the advertised speedup relevant to our workload?
    response: Run a controlled pilot on the customer's own models and compare accuracy, runtime and compute cost.

competitors

  • name: Cadence
    website: https://www.cadence.com/en_US/home/tools/system-analysis/thermal-solutions/celsius-studio.html
    why: Celsius Studio provides thermal and multiphysics analysis across electronic design levels.
  • name: Ansys
    website: https://ansys.synopsys.com/products/electronics/ansys-icepak
    why: Icepak simulates electronic cooling and thermal behavior from chip to system.
  • name: Keysight
    website: https://www.keysight.com/us/en/products/design-engineering-software/electronic-design-automation/rf-suite/thermal-design-heatwave.html
    why: Provides full-chip electrothermal simulation in IC design flows.

keywords

  • AI chip thermal simulation
  • semiconductor electrothermal simulation
  • multiscale physics simulation
  • GPU accelerated thermal solver
  • chip package thermal modeling
  • EDA simulation acceleration

voice

  • technical
  • quantitative
  • engineering-led

signals

  • text: A chip team is designing higher-power AI silicon or advanced packages and expanding thermal analysis capacity.
  • text: A simulation group is constrained by model runtime or labor-intensive setup.

stack

  • GPU acceleration

pricing

model: unknown

motion

sales_led

channels

  • website

confidence

medium

Founders

  • name: Connor McClellan
    title: Founder/CEO
    role: CEO
    bio: Stanford EE PhD grad now building an AI physics simulation platform at DeepSim to solve the frustrating parts of my PhD. I'm also really into espresso making and will be happy to make you your best cup of coffee.
  • name: Alexander Gabourie
    title: Founder/CTO
    role: CTO
    bio: Alex is a co-founder of DeepSim, Inc., where he develops AI physics simulators. He earned his PhD in Electrical Engineering from Stanford, specializing in nanoscale thermal simulations, and brings over a decade of experience in the semiconductor industry.
  • name: Chuck Koroglu
    title: Founder
    role: Founder
    bio: Stanford EE PhD with background in semiconductor engineering, and physics modeling of integrated circuits. Excited about transforming the landscape of engineering simulations with new technology that combines physics, experimental data and AI.

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