Skip to content
forked from JakobRat/RALF

Reinforcement learning assisted analog layout design flow.

License

Notifications You must be signed in to change notification settings

iic-jku/IIC-RALF

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

RALF - Reinforcement Learning assisted Automated analog Layout design Flow

Please read and cite this paper about RALF: Jakob Ratschenberger, Harald Pretl. RALF: A Reinforcement Learning Assisted Automated Analog Layout Design Flow. TechRxiv. June 03, 2024. DOI: 10.36227/techrxiv.171742468.82130474/v1 link.

As part of a master's thesis, at the Institute for Integrated Circuits (IIC), Johannes Kepler University, Linz, Austria, an automated analog layout design flow was developed.

The input of the flow is a netlist in the SPICE format composed of devices using the SkyWater Technologies SKY130 process design kit. Optionally, a file in the json-format that contains information for the routing task, like minimum wire widths, can also be specified. From the netlist, the circuit is captured and converted into an internal data structure, that is capable for the tasks of the remaining flow. The subsequent stage annotates devices which are forming smaller circuits and match those in a precompiled database. Thus, these precompiled circuits allow the finding of differential pairs, differential loads, cross-coupled pairs, and in series connected resistors, so called R-strings. From the annotated circuit, the primitive cells are instantiated, by the use of the parametrized cell generator available in MAGIC. The positions of the cells in the layout are then found by using a reinforcement learning algorithm (or optionally by using a simulated annealing algorithm), such that they minimize a cost function based on the estimated total wire length and routing congestion. After the placement is fixed, a two stage routing-algorithm connects the devices. The first stage is a wire-planning algorithm which plans the routes on a rough tile-based grid and provides a guidance to the second stage, which is a detailed router. That one lays out the actual resources by using a gridless approach based on obstacle expansion. The output of the whole flow is a .mag-file which contains the placement, and a Tcl script for generating the routing in MAGIC.

Getting started

Step 0: Prerequisites (Recommended)

Step 0.1: Prerequisites (Optional)

  • SKY130 PDK
    • For easy installation checkout volare
  • MAGIC
  • Python >= 3.9 with the installed requirements
  • Path to the sky130A pdk set under $PDKPATH, this can look like as follows
export PDKPATH=/home/pdks/sky130A

Step 1: Clone the repository

git clone https://github.com/iic-jku/IIC-RALF

Step 2: Add your circuits netlist

To design your circuit, add the circuits-netlist (only .spice formats are supported) to the Circuits folder.

Netlist prerequisites

  • The top-circuit isn't a subcircuit.
  • The netlist only contains the devices
    • sky130_fd_pr__nfet_01v8
    • sky130_fd_pr__pfet_01v8
    • sky130_fd_pr__cap_mim_m3_1
    • sky130_fd_pr__cap_mim_m3_2
    • sky130_fd_pr__res_xhigh_po_0p35
  • E.g. a valid netlist looks like
    x1 Vin Vout1 Vdd Vss inv
    x2 Vin2 Vout Vdd Vss inv
    XR1 Vout1 Vin2 Vss sky130_fd_pr__res_xhigh_po_0p35 L=2 mult=1 m=1
    XC1 Vin2 Vss sky130_fd_pr__cap_mim_m3_1 W=4 L=4 MF=1 m=1

    .subckt inv A Y Vdd Vss
    XM1 Y A Vss Vss sky130_fd_pr__nfet_01v8 L=1 W=1 nf=1 m=1
    XM2 Y A Vdd Vdd sky130_fd_pr__pfet_01v8 L=1 W=3 nf=3 m=1
    .ends
    .end

Step 2: Do a placement

There are two supported placement mechanisms:

  • Reinforcement learning based (main_RL_placement.py)
  • Simulated annealing based (main_RP_placement.py)

To do a placement, adapt the global variables according to your circuit, and run the script in a shell. The most valuable ones are

  • CIRCUIT_FILE: Defines the input SPICE-netlist.
  • CIRCUIT_NAME: Defines the name of the top-circuit and top-cell.
  • NET_RULES_FILE: Defines the net-rules file in the json-format, to specify different net-widths. If not available set the variable to None.
  • N_PLACEMENTS: Defines the total number of performed trial placements.

For the reinforcement learning based placement run:

python3 main_RL_placement.py

For the simulated annealing based placement run:

python3 main_RPS_placement.py

The placed circuit will be stored under PlacementCircuits/<circuit_name>_placement.pkl.

Step 3: View the placement in Magic

To view the placement in Magic run the script main_place_circuit.py.

python3 main_place_circuit.py

Don't forget to adapt the variable CIRCUIT_NAME to your circuits name!
The generated Magic file of the placement will be located under Magic/Placement/<CIRCUIT_NAME>.mag.

Step 4: Do a routing

The routing of an already placed circuit can be performed by running the script main_routing.py.

python3 main_routing.py

To use the negotiation based wire-planner before the detailed router set the variables

  • PLAN_WIRES=True, to activate the planner
  • N_PLANNING_ITERATIONS, for defining the number of planning iterations
  • GCELL_LENGTH, for defining the width and height of a grid cell (150 is recommended)
  • LAYERS, for defining the usable layers (['m1','m2','m3','m4'] is recommended)

Don't forget to adapt the variable CIRCUIT_NAME to your circuits name!\

Per default, the script generates a .tcl-file located under Magic/Routing/<CIRCUIT_NAME>_routing.tcl.
To view the routing, run the main_place_route_circuit.py script. Alternatively the placement can be first viewed in Magic by

magic Magic/Placement/<CIRCUIT_NAME>.mag

and then routed by using the Magic shell:

source Magic/Routing/<CIRCUIT_NAME>_routing.tcl

Net-rules file

A net-rules file contains information for the routing stage.

  • To specify the needed minimum width of a nets wires put the lines
["MinNetWireWidth",
   {
       "net" : <SubCircuit_Instance>.<Net_name>,
       "min_width" : <min_width>
   }
]

If the net is located in the top-circuit the prefix <SubCircuit_Instance>. hasn't to be specified. The variable <min_width> defines the minimum width of the wire, whereby the unit of the width is in $\lambda = 10\mathrm{nm}$.

E.g. for the netlist

   x1 Vin1 Vout Vdd Vss buf
   XR1 Vin Vin1 Vss sky130_fd_pr__res_xhigh_po_0p35 L=2 mult=1 m=1
   XC1 Vin1 Vss sky130_fd_pr__cap_mim_m3_1 W=4 L=4 MF=1 m=1

   .subckt buf A Y Vdd Vss
   x1 A Y1 Vdd Vss inv
   x2 Y1 Y Vdd Vss inv
   .ends

   .subckt inv A Y Vdd Vss
   XM1 Y A Vss Vss sky130_fd_pr__nfet_01v8 L=1 W=1 nf=1 m=1
   XM2 Y A Vdd Vdd sky130_fd_pr__pfet_01v8 L=1 W=3 nf=3 m=1
   .ends
   .end

the net Vout1 gets accessed by

["MinNetWireWidth",
   {
       "net" : Vout1,
       "min_width" : 20
   }
]

The net Y1 in the sub-circuit buf of device x1 gets accessed by

["MinNetWireWidth",
   {
       "net" : x1.Y1,
       "min_width" : 20
   }
]

Example - Differential amplifier

In the following, the layout generation flow for the circuit Circuits/Examples/DiffAmp.spice, will be presented.

Schematic

Placement

Run for example

python3 main_RP_placement.py

and place the circuit in Magic, per

python3 main_place_circuit.py

Resulting placement:

Routing

Run

python3 main_routing.py

and show the routing in Magic per

python3 main_place_route_circuit.py

Resulting routing:

About

Reinforcement learning assisted analog layout design flow.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 84.6%
  • Tcl 14.9%
  • Shell 0.5%