1. A method comprising: controlling operation of a semiconductor manufacturing system with a neural network to schedule processing of one or more workpieces, the semiconductor manufacturing system including at least two workpiece handling robots in at least two processing planes, the robots capable of handing workpieces between the at least two planes; creating a data structure for one of the one or more workpieces, the data structure including an identity of the one or more workpieces and one or more fields for storing state information of the one or more workpieces; and receiving data from the semiconductor manufacturing system as inputs to the neural network; wherein the neural network is configured to effect modification of a processing task that changes a physical characteristic of a structure of the one or more workpieces in scheduled processes for each individual workpiece based on the state information related to the physical characteristic of the structure of respective ones of the one or more workpieces and the data received from the semiconductor manufacturing system; and wherein the neural network outputs weights for one or more states of a finite state machine where the one or more states provide control signals for controlling operation of a semiconductor manufacturing system, and the neural network separates processing that reconfigures the state machine from operation of the finite state machine such that the finite state machine is enabled to operate independent of the neural network for allowing the neural network to evaluate outputs where processing time of the neural network extends beyond a time increment of the finite state machine.