mpw-008 / CAP2M Capacitor mask

the CAP2M shapes of all of mpw-008

40 of 40 dies rendered.

121,050 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (4.5 MB in total, largest 204.6 kB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-008_CAP2M_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-008_CAP2M_reticle.png
the stitched reticle, full resolution
3.6 MB 68bb650cb912806acc46b76506728f0e0df5a7cff45f800087252dc41e6dd410d57710be 4fe39316e367274450c634b6e4be1be2e8a5fb72635f0c25a094c2349c5d72bd
mpw-008_CAP2M_reticle_overview.png
the same image scaled to fit 4096 px
61.6 kB 9f4cee0ccd89cf042b7025cdac5218f1d28f9db9a8e326fc04b2ed512c44e63b8beb42b0 27f7b41275a69a8edd73b871100eab97b51c532baa73b6f293602b5b43faa707
mpw-008_CAP2M_tiles.json
where every tile belongs, and its checksum
13.5 kB 6def5454a3be032c1b361a17135099cc0c25a46a17f3abb9e1c55110a0460c51bb3cf589 c5dd180a5a7d02d3fd036b2e983529d12f0eccc31aaff640515c1f595c7dad84
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 wishbone_pio 7576×107760, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A2 iit_indore_mm 7576×107767576, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A4 riscduino-dcore_d4_ 7576×1077622728, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A5 riscduino-qcore_q3_ 7576×1077630304, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A6 caravel_mpw8 7576×1077637880, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 111.0 kBbb703af29b756ec4818bb18dab77a904f001e9539ce44930fc54ac84f7be8548
B2 sramtestvehicleg4 7576×107767576, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B3 reram_crossbar_rerun 7576×1077615152, 10776 204.6 kB3d28f5c9dc4beca8eba0f44a9d194f70b527d9e0cca217f5bab086543684e5df
B4 tinytapeout_skullart 7576×1077622728, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B5 soomrv__mpw-8_ 7576×1077630304, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B6 clock_divide_selector 7576×1077637880, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B7 reram_module_mpw8 7976×1077645456, 10776 106.9 kB60ca2196d8ee88e8743dee718a892b494aa1384fc91ecb15b30d24679dbbd62d
C1 shapingthewaves_8 7576×107760, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C2 leosoc 7576×107767576, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C3 decrypt_aes128 7576×1077615152, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C4 approximate_adder_axha 7576×1077622728, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 107.4 kB5f34ced128bdf17319b0df7528301bff5dde995d4771cc5bdc782f91bc060073
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D2 hyperram_controller_for_sky130 7576×107767576, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D3 hardened_alu 7576×1077615152, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D6 gf180_vs_sky130 7576×1077637880, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 106.9 kB60ca2196d8ee88e8743dee718a892b494aa1384fc91ecb15b30d24679dbbd62d
E1 iiitb_sd_mpw8 7576×107760, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E2 iiitb_sfifo 7576×107767576, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E3 iiitb_alu 7576×1077615152, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E4 ppcpu2 7576×1077622728, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E5 mega4_reram 7576×1077630304, 43104 106.5 kBf601819dba9a0b7d55a2353819fffe7e2d0c0c5c64bf8aef420ab65c55692acd
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E7 sky130cla 7976×1077645456, 43104 106.9 kB60ca2196d8ee88e8743dee718a892b494aa1384fc91ecb15b30d24679dbbd62d
F1 uetrv_esoc_v2 7576×111760, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F2 sequencedetection1010 7576×111767576, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F3 testasynctrimux-mpw8 7576×1117615152, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F6 arbiter_puf 7576×1117637880, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F7 unigate-sky 7976×1117645456, 53880 110.9 kBea0be884384754165c29bf0468e1ee1a1f67ad1c71ab09fb6c962f232b604ebe

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About CAP2M on mpw-008

Resolved: reticle 5CS8017AC, run mpw-008, mask CAP2M

Mask layer
CAP2M — Capacitor mask
Process step
152 — Capacitor mask
Drawn layer
97:44

mpw-008 — SKY130 MPW-8

Reticle set
5CS8017AC
Run date
2022-11-19
Submissions closed
2022-12-31
Projects
144
Identification
inferred — Sequence after 8016=MPW-7 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-8 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-008/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.