Physical AI is technology in which software understands the state of the real world, makes a decision, and then moves or changes a physical system. Robots and autonomous vehicles are the most familiar examples. A robot perceives its surroundings, estimates the current state, and chooses its next motion. An autonomous vehicle uses sensors to understand the road and controls steering, acceleration, and braking. In both cases, software does more than process information: it understands a physical system and turns that understanding into action. By the same definition, semiconductor equipment is also a target for Physical AI.
- Physical AI connects software understanding of a physical system to real action, and applies not only to robots and autonomous vehicles but also to semiconductor manufacturing equipment as well.
- Robots, cars, and semiconductor tools look different, but they share the same structure: sense → predict → decide → act.
- Semiconductor equipment already has sophisticated hardware. The key is not to build a new machine, but to add state understanding, prediction, and decision-making to existing machines.
- Technology stacks developed in robotics and autonomous driving—state estimation, sensor fusion, and control—can extend into manufacturing. Amously provides these decision-making capabilities for semiconductor equipment and manufacturing systems.
What do robots, cars, and semiconductor equipment have in common?
The three systems look completely different. Robots pick up and move objects, cars travel on roads, and semiconductor tools process wafers inside chambers. But intelligence has to do similar work in each case: understand the current state through sensors, predict what is likely to happen, and decide what action is needed. When the actual result differs from the prediction, the way decisions are made must also be adjusted.
The targets differ, but the structure is the same: understand state through sensors, make a decision, then affect the physical system.
In robots, position, speed, force, and motion path are major control targets. In cars, steering, acceleration, and braking are controlled. In semiconductor equipment, conditions such as temperature, pressure, gas flow, power, and process time are controlled. The targets differ, but the structure is the same: sense state, decide, and act on a physical system.
| Robot | Car | Semiconductor equipment | |
|---|---|---|---|
| Physical system | Robot arm, mobile robot, end effector | Vehicle, steering, acceleration/braking systems | Process tool, process chamber, wafer |
| Primary sensors | Camera, distance sensor, force sensor | Camera, radar, lidar, GPS | Temperature, pressure, gas flow, power, equipment sensors |
| Primary control variables | Position, speed, force, motion path | Steering, acceleration, braking | Process time, temperature, pressure, gas flow, power |
| Role of intelligence | Understand the environment and decide robot motion | Understand driving conditions and decide safe driving actions | Estimate process state, predict results, and optimize process control |
| Primary goal | Accurate task execution | Safe driving | Stable process quality and high productivity |
| Key challenge | Position error, uncertain contact, unexpected situations | Complex environments and strict safety requirements | Invisible processes, many variables, high cost of failure |
The targets differ, but the structure is the same: sense state, make a decision, and affect a physical system.
| Robot | Vehicle | Semiconductor equipment | |
|---|---|---|---|
| Physical system | Robot arm, mobile robot, end effector | Vehicle, steering, acceleration and braking | Process equipment, chamber, wafer |
| Main sensors | Camera, distance, force | Camera, radar, lidar, GPS | Temperature, pressure, gas flow, power, equipment sensors |
| Main control variables | Position, speed, force, path | Steering, acceleration, braking | Process time, temperature, pressure, gas flow, power |
Machine automation and intelligence are different problems
Having a car does not make autonomous driving possible. Even with cameras, brakes, and steering hardware, software is needed to interpret the surroundings and decide the next action. Robots are the same. A robot arm and motors alone do not perform a task; the system must locate objects, plan paths, and regulate force.
Semiconductor equipment already has very sophisticated hardware, with many sensors and control devices executing processes under predefined conditions. What is still needed is the ability to understand the current state, predict the result, and decide the necessary action. The problem is less about building a new machine than about adding intelligence to machines that already exist.
Robotics and autonomous driving have developed technologies such as state estimation, sensor fusion, simulation, control, and online learning over many years. They cannot simply be copied into semiconductor manufacturing: the data and physical phenomena are different, and the cost of a wrong decision can be much higher. But the way the problem is framed is similar.
Estimate invisible states from sensors, predict future outcomes, and decide actions within a constrained range. When the environment changes, adjust the decision criteria again. Instead of controlling robot motion, control process conditions; instead of interpreting road conditions, interpret equipment and process state. This is how the Physical AI technology stack can extend into manufacturing.
From equipment automation to intelligent manufacturing systems
Semiconductor manufacturing is already highly automated. But today's automation is strongest at repeating predefined commands. What is needed next is the ability for equipment and manufacturing systems to understand their current state and respond to change.
The engineer's role changes as well. Instead of manually checking every data point and adjusting conditions each time, engineers define the range in which the system may decide, set safety criteria, and supervise the decisions that matter most.
Amously is building software that provides this decision-making layer for semiconductor equipment and manufacturing systems. We are implementing, for the semiconductor industry, what the concept of Physical AI developed in robotics and autonomous driving should look like on the manufacturing floor.
※ This article is a perspective piece explaining how the concept of Physical AI developed in robotics and autonomous driving can be implemented in semiconductor manufacturing. Actual deployment depends on process and equipment characteristics, available data, safety requirements, and operating policy.